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Introduction to Clustering Methods

Clustering methods, such as Hierarchical, Partitioning, Density-based, Model-based, and Grid-based models, assist in grouping data points into clusters. These techniques use various methods to determine the appropriate result for the problem. Clustering helps to group data points into similar categories, with each sub-category further divided to facilitate the exploration of query output.

Explain Clustering Methods.

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Hierarchical methods

Partitioning methods


Model-based clustering

Grid-based model

Here is an overview of the techniques used in data mining and artificial intelligence.

1. Hierarchical Method

This method creates a cluster by partitioning both top-down and bottom-up. Both these approaches produce dendrograms that make connectivity between them. The dendrogram is a tree-like format that keeps the sequence of merged clusters. Hierarchical methods have multiple partitions concerning similarity levels. Agglomerative hierarchical clustering and divisive hierarchical clustering divide the data into clusters. These methods create a cluster tree through merging and splitting techniques. Agglomerative clustering merges clusters, while divisive clustering separates them.

Agglomerative clustering involves:-

They were initially taking all the data points and considering them as individual clusters starting from a top-down manner. Analysts merge these clusters until they obtain the desired results.

The following two similar clusters are grouped to form a huge single cluster.

Again calculating proximity in the huge cluster and merging the similar clusters.

The final step involves merging all the yielded clusters at each stage to form a final single cluster.

2. Partitioning Method

The main goal of partition is relocation. They relocate partitions by shifting from one cluster to another, which makes an initial partitioning. It divides ‘n’ data objects into ‘k’ numbers of clusters. This partitional method is preferred more than a hierarchical model in pattern recognition.

The following criteria are set to satisfy the techniques:

Each cluster should have one object.

Each data object belongs to a single cluster.

The most commonly used Partition techniques are the K-mean Algorithm. They divide into ‘K’ clusters represented by centroids. Then, each cluster center is calculated as a mean of that cluster, and the R function visualizes the result.

This algorithm has the following steps:

Selecting K objects randomly from the data set and forming the initial centers (centroids)

Next, assign Euclidean distance between the objects and the mean center.

Assigning a mean value for each individual cluster.

Centroid update steps for each ‘k’ Cluster.

3. Density Model 4. Model-Based Clustering

This model combines two or three clusters together from the data distribution. The basic idea behind this model is to divide data into two groups based on the probability model (Multivariate normal distributions). In this model, we assign each group as concepts or classes and define each component using a density function. We use Maximum Likelihood estimation to find the parameters to fit the mixture distribution. We model each cluster ‘K’ using a Gaussian distribution with a mean vector µk and a covariance vector £k, each having two parameters.

5. Grid-Based Model

The approach considers objects to be space-driven by partitioning the space into a finite number of cells to form a grid. Then, the approach applies the clustering technique with the help of the grid for faster processing, which typically depends on cells rather than objects.

The steps involved are:

Creation of grid structure

Cell density is calculated for each cell

Applying a sorting mechanism to their densities.

Searching cluster centers and traversal on neighbor cells to repeat the process.

Importance of Clustering Methods

Having clustering methods helps restart the local search procedure and removes the inefficiency. In addition, clustering helps to determine the internal structure of the data.

This clustering method has been used for model analysis and vector region of attraction.

Clustering helps in understanding the natural grouping in a dataset. They aim to make sense of partitioning the data into some logical groupings.

Clustering quality depends on the methods and the identification of hidden patterns.

They play a wide role in applications like marketing economic research and weblogs to identify similarity measures, Image processing, and spatial research.

They are used in outlier detections to detect credit card fraudulence.


Experts regard clustering as a universal task that involves formulating optimization problems to address various issues. It plays vital importance in the field of data mining and data analysis. We have seen different clustering methods that divide the data set depending on the requirements. Researchers mainly rely on traditional techniques such as K-means and hierarchical models for their studies. They apply cluster areas in high-dimensional states, which presents a potential area for future research.

Frequently Asked Questions (FAQs)

Answer: Several types of clustering methods exist, including hierarchical clustering, k-means clustering, density-based clustering, and model-based clustering. Each method has its strengths and weaknesses, and the choice of method depends on the data’s characteristics and the analysis’s goals.

Answer: Clustering can help identify patterns and relationships in data that may not be apparent from simple visual inspection. It can also segment customers or products for targeted marketing, identify anomalies or outliers in data, and reduce the dimensionality of large datasets.

Q3 What are the limitations of clustering?

Answer: Clustering can be sensitive to the choice of distance metric or similarity measure, and the number of clusters can be difficult to determine. The clustering results can also be highly dependent on the quality of the input data and the assumptions underlying the clustering method.

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How To Represent Ruby Ranges Using Various Methods?

Introduction to Ruby Ranges

Ranges in the Ruby are a way of handling some sequences , these sequences are anything which we use in our day to day life, for example, a to z, A to Z or from 1 to 100 all these are ranges. In Ruby, ranges can be used for three broader categories like for sequence, for conditions and also we can use the ranges for the interval, in general if you wanted to use the number from 1 to 100 then instead of writing each number we can simply put the range from 1 to 100(1..100) using double dots (..)or in case if we want to ignore the highest number which means we want to have range from 1 to 99 we can use 1…100 which is triple dots(…).

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We can have three different syntax for the ranges, the three different syntaxes are given below:

Syntax for the use of ranges as the sequences:

Here we can use it in case if we want some sequence from on start range to another end value. See the below syntax we have assigned some sequence from start to the end. You can assign anything from numeric ranges to string ranges for example, a..z or 1..100.

sequences = (start ..end).to_a example (a..d).to_a (output will be a,b,c,d)

Syntax for the use of ranges as the conditions:

In this use the case and you will be able to get the range of the number. In the below example we have one value it can be anything either numeric or string and it will be going into the case block of ruby where it will be check with a certain defined syntax we are using it for any comparison or for certain specific conditions, for example if you have one number 40 and you wanted to know the range of the number then you can ranges. Once the value match the particular ranges we can print the message or perform a certain operation as we have information about the ranges.

value = any numeric or string value result = case value when chúng tôi then "messages according to the conditions" when chúng tôi then "messages according to the conditions" when chúng tôi then "messages according to the conditions" end

Syntax for the use of the ranges as the Intervals:

for this we can use the operator ===, in the below syntax we can see the uses of the === for getting the value falling under the given range or not. For example a..g ===f and the output will be true as f is falling under the range a to g.

(start..end) === compare any value (example , a..g===f ,output will be true)

Note: We can also have some custom angles, for example, range of (abc..abe) and the output will be abc, abd, abce.

How to Represent Ruby Ranges Using Various Methods?

Below we learn how to represent Ruby Ranges using Various Methods with the help of examples:

Example #1 – to_a Method

This method used to convert the ranges into an array. With the help of this method we can get the array as many times we need to create a large array with writing many attributes into that, with help of the method to_a we will get the array easily. See the below example along with the output of the screen.


puts "#{("abc".."abe").to_a}" puts "#{(1..10).to_a}"


Example #2 – (min,max and include) Method

This method in the ranges returns the minimum value from the given range. In case if we have a large set of range in that case it will be very useful to fetch the minimum or the maximum value. we have also used the method include on the ranges which checks for the number or letter or any substring fall under the ranges. See the below example we have used min, max and include along with output screen.

alphabets = "a".."r" puts "The letter n is in available in the given range #{alphabets.include?("n")}" alphabet1 = alphabets.min alphabet2 = alphabets.max puts "The lowest value of the alphabet is #{alphabet1}" puts "The largest value of the alphabet is #{alphabet2}"


Example #3 – Reject Method

Here we are using the reject method of the range, with the help of this method we can return the range of letters. In the below example we are simply rejecting the letter if it is less then “c”, in block of code it will start rejecting from c till the last range value , which is “e”.


alphabets ="a".."e" puts "Rejected letters start from c are  #{rejected}"


Example #4 – Each Method

Each method for the range is used to iterate over all the values inside the range. In the below example we are printing all the value of the range from a to e. Please see the below example with the screen of output.

alphabets ="a".."e" puts "The list of alphabet is  #{alphabet}" end


Example #5 – Case Method

With the help of the case we check for the particular value ranges, in the below example we have a mark of a student and we wanted to understand the grade of the student. The case attribute “when” will check for the range each time and if match the particular range it will print the message for that range. Please follow the below example along with the screen of output.


mark =67 studentFinalGrade = case mark when 0..28 then "The student got very less mark and failed in this paper" when 29..40 then "The student got average marks and he got pass with D grade" when 41..60 then "The student is ok and he got C grade" when 61..81 then "The student is very good and he got B grade" when 82..100 then "The student is very excellent and he got A grade" else "The mark is not valid and no grade can be assigned" end puts  studentFinalGrade



From these tutorials we learned about the ranges in Ruby, we understand the uses and the core concepts like its method which are the main plus points, as with the help of ranges method we can do comparison to conversion into array to the given set of ranges, which will make developer life more comfortable.

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Importance Of Segmentation And How To Create One?

Average is one of the biggest enemy of analysts!

Why do I say so?

The amount of reporting which happens on averages is astonishingly high. Sadly, working with averages never reveals actionable insights until broken down in segments. Let’s go through a typical example to demonstrate what I am saying:

Let us assume that you head Customer Management division of Credit cards for a bank. Two metric of immense importance to you are:

Monthly spend people do on their credit cards – Indicates usage of credit card for customers

How much of their credit limit are customers utilizing? – Increasing trend might mean increasing risk or higher satisfaction. Decreasing trend might mean the other way.

You look at the following report and feel that everything is under control. You reach a quick conclusion that there is no problem in continuing your engagement as they run today:

In a practical scenario, these metrics would be an aggregate across various cards, but for simplicity, lets say that there are 2 kinds of cards:

Card A: Aimed towards people with good credit history. They will tend to have higher credit limits, lower risk and hence lower lending interest rates.

Card X: Aimed towards people new to Credit or people with bad Credit history. These will have lower Credit limits, higher risk and hence higher lending interest rates.

Again, just to simplify, lets assume that you have an even mix. The minute you split the metrics by segments, a different story emerges:

As you can see, what is actually happening is very different from what you would interpret from Average metrics. Actually, usage of cards with your low risk customers is on decline where as on the high risk customers is on increase – might be a scary situation!

[stextbox id=”section”]What is segmentation?[/stextbox]

Segmentation is a process of breaking a group of entities (Parent group) into multiple groups of entities (Child group) so that entities in child group have higher homogeneity with in its entities.

Following is a simple example of customer segmentation for a bank basis their age:

In this case you take a single group (customers of bank) and segment them in 5 child groups (basis their age). Incorporating this segmentation in your analysis can then drive various insights and ultimately actions in interest of your business like:

Are customers buying right kind of products?

What are the opportunities to sell an additional product to the customer? If the person became a customer as “Young Professional“, has the need changed as he is now a “Married Professional“

What kind of marketing channels would appeal to which kind of customers? How much to spend in each channel?

General guideline to create the child groups is that they should be “Heterogeneous with other groups, but homogeneous with in group“.

[stextbox id=”section”]How to create a segmentation?[/stextbox]

While there are multiple techniques to create a segmentation, the focus of this post is not on technical knowledge. I’ll layout the process used to create a segmentation and keep the technical details for a later point. This will enable you to create and implement a segmentation, even if it is not the best technically. You can obviously learn more details about the techniques and apply them in conjunction with the process mentioned here:

Step 1: Define the purpose of the segmentation. How do you want to use this segmentation? Is it for new customer acquisition? Managing a portfolio of existing customers? or Reducing credit exposure to reduce charge-offs? Every segmentation is created for a purpose. Until this purpose is clear, you will not be able to create a good segmentation.

Step 2: Identify the most critical parameters (variables) which influence the purpose of the segmentation. List them in order of their importance. Now, there are multiple statistical techniques like Clustering, Decision tree which help you do this. If you don’t know these, use your business knowledge and understanding to come out with the list. For example, if you want to create a segmentation of products and focus on products which are most profitable, most critical parameters would be Cost and Revenue. If the problem is related to identifying best talent, the variables would be skill and motivation.

Step 3: Once these variables are identified, you need to identify the granularity and threshold for creating segments. Again, these can come from the technique developed, but business knowledge could be deployed equally well. As a general guidance, you should have 2 – 3 levels for each important variable identified. However, it depends on complexity of problem, ability of your systems and openness of your business to adapt a segmentation. Some of the simple ways to decide threshold could be:

High / Medium / Low with numerical thresholds

0 / 1 for binary output

Vintage / Age of customers

Step 4: Assign customers to each of the cells and see if there is a fair distribution. If not, tweak the thresholds or variables. Perform step 2, 3 and 4 iteratively till you create a fair distribution.

Step 5: Include this segmentation in your analysis and analyze at segment level (and not at macro level!)

[stextbox id=”section”]Example of creating Segmentation:[/stextbox]

Let us say that you want to create HR strategy to identify which employees should be engaged in what manner so that you are able to reduce attrition and offer what the employee actually wants.

Define purpose – Already mentioned in the statement above

Identify critical parameters – Some of the variables which come up in mind are skill, motivation, vintage, department, education etc. Let us say that basis past experience, we know that skill and motivation are most important parameters. Also, for sake of simplicity we just select 2 variables. Taking additional variables will increase the complexity, but can be done if it adds value.

Granularity – Let us say we are able to classify both skill and motivation into High and Low using various techniques. This creates a simple segmentation as mentioned below:

If the distribution is skewed highly in one of the segments, we can change the threshold to define High and Low and re-create the segmentation.

Finally, you can now start analyzing employees to answer following questions:

Which kind of employees are having highest attrition? Is this HL / LH / LL or HH?

What is the average life span for each of these categories?

How many training each of these segments get in a year?

How many HH employees have been recognized for their work and contribution? What can be changed?

Hope this gives you a fair idea about creating and implementing a segmentation.

[stextbox id=”section”]A few additional notes:[/stextbox]

Before closing the article, would like to mention a few additional points to keep in mind:

Try and keep a fair volume distribution in various segments. If this does not happen, then you will end up analyzing on this data, which can result in wrong inferences.

Segmentation is always done as a means to achieve something. It can not be an objective in itself. So before starting any segmentation, always ask are you clear about the objective.

Techniques of segmentation help, but you can achieve more than 70% of results with a good business understanding.

So next time if you see any reporting happening at an overall level, STOP. STOP and think what you might be looking over and how can you improve this to bring out more actionable insights.

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6 Methods Of Rebranding And Avoiding The Pitfalls

Introduction To Rebranding Exercise

Have you heard of companies such as BackRub, Research In Motion, Brad’s Drink, Tokyo Tsushin Kogyo? Not likely unless you are closely tracking companies or doing some research. These are the old names of Google, Blackberry, Pepsi, and Sony, respectively! In this article, we will discuss the Rebranding Exercise in detail.

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Some of the leading brands in the world had changed their logos, packaging, and brand names much before they became famous; as the result, not many people know their old name. The process of changing logos, brand names, packaging, and changing the focus of marketing promotion is called the Rebranding Exercise. This is done with the intention of creating a new brand identity and differentiating from the competition.

6steps to successful rebranding

Here are six steps to successful rebranding exercise and improved market performance:

1. You have outgrown your name or expanded your operations

Imperial Tobacco Company of India Ltd (ITC Ltd) was originally a tobacco company under British ownership registered in 1910. When it fell into Indian hands and as it began to produce and market more products and entered into new businesses- it changed its name to India Tobacco Company Ltd in 1970 before it was renamed I.T.C Ltd. It added a range of products – consumer goods, agarbathis or incense sticks, notebooks, paper boards, stationery products, hotels, agri-business, and information technology. Subsequently, the name was changed to ITC Ltd without the full stops.

IBM was originally known as Computing-Tabulating-Recording Company (C-T-R) following the merger of three businesses in 1911. Thereafter, it was rechristened by International Business Corporation in 1924, following which it came to be known as IBM. The major reason for a change in branding was due to geographical and functional expansion.

ICICI Bank, the largest commercial bank in India, was incorporated in 1995 as Industrial Credit and Investment Corporation of India, promoted by the World Bank, Indian Government, and industry representatives. The objective was to provide finance to Indian businesses. However, over the years, it ventured into commercial banking, insurance, credit cards, home loans, securities, and several other businesses that the original name did not do justice to its scale of operations. Now it is known as ICICI Bank.

When you add more products or businesses or expand operations, an original brand name may not be comprehensive enough to encompass them all. It is a valid reason to go for a rebranding exercise.

Sometimes rebranding exercise has to be done because they are not internet or search engine friendly. Would the computer firm Apple be named that way if it were launched in the internet era? According to analysts, the brand name that closely reflects the industry or product is more likely to succeed. The rebranding exercise also necessitates making changes to keywords, making changes to search engine optimization and content/articles, blogs related to it.

Infosys denotes information systems that gel with their functional domain. Likewise, Hindustan Computers Ltd (HCL), which was originally into computer hardware only, found the name inconvenient when it ventured into software development and systems. Thereafter it was rechristened HCL.

2. Rebranding exercise due to a negative image

KFC was originally Kentucky Fried Chicken but following the negative association of ‘fried’ food. Italian chocolate brand Italo Suisse had changed to ISIS three years ago but had to quickly retrace its steps soon as a terrorist group by the same name emerged. A bad reputation cannot be changed by just change in nomenclature alone. Sometimes, it may require a change in product quality, positioning, and re-engineering.

Wal-Mart came to be associated with cheap brands and that necessitated a change in its tagline from ‘Always Low Prices’ to ‘Save Money, Live Better. For Wal-Mart, it was not just changed in the tagline that mattered. They made changes to interiors providing a great new in-store experience. It helped fetch them a Rebrand 100 Global Award and became the world’s largest corporation in terms of revenue in 2010. Wal-Mart realized that focusing on improved lifestyles and living helps create a positive feeling among consumers for its products sold.

Philip Morris rebranded to Altria to wipe out the negative perceptions of tobacco having an impact on its other businesses, especially food. The 1196 plane crash led ValuJet to rename itself as Air Tran Airways.

When Ramalinga Raju of Satyam Computers was caught for forging a balance sheet, the ownership subsequently went to Mahindra. It changed the name to Mahindra Satyam to ward off negative perceptions of the brand.

3. Don’t make haste with the rebranding exercise, do market research

When a brand name is suddenly changed, it could have implications for the consumer who may be confused as to what happened with the previous brand. Brand consultants suggest spending at least six months to more than two years to launch the new brand with adequate promotions and campaigns.

Master Card had a two-circle logo which had become quite familiar, but a bubble or circle was added with added elements. This was not received well by the public, and it had to recall.

Likewise, Radio Shack, the shopping center, removed Radio from the name and did a #200 mn campaign for the new name The Shack, throwing the company into a financial crisis. Likewise, Pizza Hut removed Pizza as part of a rebranding process, only to bring Pizza back.

Tropicana, the Pepsi brand, went for a major packaging change, but the new one looked like a generic fruit juice, thus resulting in a drop in sales. Pepsi is reported to have lost about $140 mn in two months.

Apparel firm Gap had to restore its logo to one seen on the right after netizens raised an outcry against the new logo shown on the left. It had to recall within a fortnight, and the company has attributed it to a lack of research and doing rebranding exercises in the wrong way.

The above examples denote haste in going ahead with rebranding exercise can cause both financial damage and loss of reputation. It would also cause a lack of continuity of brand in the minds of consumers.

4. Hold interviews with customers, stakeholders, and employees

A rebranding exercise should not emerge just in the minds of the management and branding consultant or agency. It should involve existing distributors, employees, customers who actually patronize the brand. Sometimes the general public can be involved by holding contests.

5. Set objectives for the rebranding exercise

The rebranding should be result-oriented- it should lead to increased market share, increased brand recall, increase sales and profits. If such variables have to be accounted for, the company has to calculate the existing market share of the company and brand and have a target to achieve.

Canara Bank went for a rebranding strategy, recently pointed out that the new logo represents the bank’s initiative to reach out to all its stakeholders, including creditors, government, depositors, institutions and the society at large. The blue color is denotive of depth and scale while Yellow shows energy, vibrancy and optimistic outlook.

6. Don’t go for fancy names or hard to pronounce names

Shakespeare said, ‘What’s in a name, a rose smells sweet by any other name’. But when it comes to business and branding, rose smells more when it is called a rose. Therefore, it is better not to take the risk and go for fancy names or hard to pronounce names. Also, avoid names that can be spelled, pronounced in different ways, making it difficult for people to search on the net. Your brand name has to be memorable, easily recalled along with a logo and not easy to be duplicated.

SyFy, the Sci-Fi channel’s rebranding exercise, bombed as it is a slang for syphilis, the sexually transmitted disease affecting men. The company justified saying that Sci-Fi was not traded mark-able and could appeal to the younger crowd than 18-24 techies. However, it received a negative reaction and turned out to be a marketing disaster.


Business history is replete with both success and failures in rebranding strategy but that shouldn’t discourage a company from doing so. Familiarity breeds contempt goes the old saying but it may not be with the brand- as it can also create a liking. In such cases, a change in a brand name can evoke strong reactions leading to loss of goodwill from stakeholders. There should be a compelling reason for rebranding strategy and some are unavoidable as in mergers and acquisitions. Rebranding strategy should convey the continuity associated with the company’s founders and principles but at the same time embody the new principles or values imbibed with the changing times as in Canara Bank’s case- bonding with stakeholders reflecting the new era.

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5 Best Signal Booster Apps And Other Methods Too

To reiterate, there is no way to boost your signal with a mobile app. The list below is meant to help you identify potential hiccups in your network that may be slowing your speeds. Alternatively, there may be apps on your phone hogging data resources, and some apps on this list help with that as well. Clearing up these types of issues can usually increase speed.


See also: The best privacy apps for Android to keep your anonymity intact

IP Tools

Network Cell Info

Price: Free / $1.49

Network Cell Info helps you find the nearest cell towers. The bad signal comes from a variety of factors. One of which may just be the distance from a tower. An app like this shows you where the towers are to see how far you are away. It should work on both GSM and CDMA carriers, and it supports dual-SIM as well. Some other features include signal measurements and a crowd-sourced signal finder. The pro version is $1.49, and you need to unlock some of the features.

See also: Our guide to the best phone plans for every type of user


See also: The best internet providers in the US

Wi-Fi Analyzer

There are a bunch of Wi-Fi Analyzers on mobile. We like this one because it’s relatively easy to use, gets frequent updates, and works really well. The app not only shows you the Wi-Fi signal from your home but those from nearby homes as well. You can get a really good sense of the network congestion in your space with this app. From there, you can adjust your router settings to broadcast on less cluttered channels, and that may help your signal issues. This one supports 2.4Ghz and 5Ghz connections and a bunch of other stuff.

Bonus: Your router’s app

Get a signal booster from your mobile carrier

Verizon knows how to hide their towers.

Sometimes there is no fix, and you need some extra hardware. Most mobile carriers have an option for a signal booster. A signal booster is a piece of hardware you install in your house. The boosters work in two different ones. One type receives a signal from the OEM and boosts it to function properly in your house. The other type connects to your house’s Internet and connects to carrier servers from there. All three major US carriers have them, and some of them do some unique stuff.

Signal boosters are generally not too expensive, and a tech will install one if you don’t have the know-how to do it yourself. The boxes should fix any weak signal issues inside of your home, but they obviously don’t work when you’re out and about. Below we have official signal boosters from the big three US OEMs, and they work a lot better than those fake signal booster apps in the Play Store.

T-Mobile’s signal booster

AT&T’s MicroCell signal booster

Verizon’s signal boosters

Use your Wi-Fi when available

Robert Triggs / Android Authority

A common tactic is to simply avoid your mobile data unless you have to. Many devices these days let you make phone calls over Wi-Fi, and a lot of people can simply make calls over Facebook Messenger or a similar data-only service. By using Wi-Fi calling and avoiding your mobile network, it doesn’t matter if your signal isn’t great indoors because you’re not using it anyway.

Examples For Queryselector() In Various Properties

What is jQuery querySelector?

jQuery querySelector is used for selecting a specific document object model (DOM) element from the HTML document, using the HTML elements like name, id, attribute, type, attribute values, class, etc. This selection activity is performed with the help of the query querySelector() method, which is used to fetch the return value as the first value identified in the CSS selector document. This function is for performing multiple operations and is known amongst the programmers for it’s the faster processing time, smaller & plain javascript code, and easier to code as well.

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Introduction to querySelector

The querySelector() method only returns the first element that matches a specified CSS selector(s) in the document. If an ID in the document is used more than once then it will return the first matching element.

Syntax of querySelector querySelector(CSS selectors)

It returns the first element that matches the specified selectors.

To return all the elements which match then we use the querySelectorAll() method.

The CSS selectors which we pass should be of string type.

It is mandatory to pass the CSS selectors.

The string which we are passing must be a valid CSS selector.

If the passed string is invalid then an SYNTAX_ERRexception is thrown.

If no match is found it will return null.

The matching of the first element is done using a depth-first pre-order traversal of the document.

Specifies one or more CSS selector to match the element.

For multiple selectors, separate with a comma.

Characters that are not part of standard CSS syntax must be escaped using a backslash character.

Examples for querySelector() Method

Below are the examples for querySelector() methods:

In jQuery, you can select elements in a page using many various properties of the element they are Type, Class, ID, Possession of Attribute, Attribute Values, etc. Below is the example of Jquery by using type.

Example #1 – Selecting by type

Explanation of the above code: In this example, we can observe that we have used two anchor tags and inside the anchor tag we have passed the hyperlink of two images. By using the querySelector(“a”).style.backgroundColor = “red”; we have passed the anchor tag (“a”) to the querySelector. In the querySelector() method if we pass the multiple selectors it will return the first element that matches the specified selectors. Though it contains two anchor tags the first anchor tag which is found, applied its style.backgroundColor = “red”; to only for first anchor tag.

Explanation of the above code: In this example also we can observe that we have used two anchor tags and inside the anchor tag we have passed the hyperlink of two images. By using the querySelector(“a”).style.backgroundColor = “red”; we have passed the anchor tag (“a”) to the query selector. This time in the querySelector() it will find out the “Desert” hyperlink first as we changed the sequence. Though it contains two anchor tags the first anchor tag which is found, applied its style.backgroundColor = “red”; to only for first anchor tag.

Example #2 – Selecting by class

In this below example we are selecting by using the class name.

Explanation of the above code: In the above example, we are using the class name and here the class name is Selector. The same class name is passed for both h2 (heading tag) and the paragraph tag. For the querySelector() method we are passing the class name it will check for the particular class name in the program. Now it has found those tags which are having the same class name as mentioned. By using the depth-first pre-order traversal of the document the matching of the first element is done. The first element in the example which contains the class name as Selector is h2 (heading tag). The querySelector() method fetches the h2 tag and by style.backgroundColor it applies the particular background color to the h2 tag.

Example #3 – Selecting by ID

In this below example we are selecting by using id.

Explanation of the above code: In the example, we are selecting by using id the id here is Selector. For the querySelector() method we are passing the id it will check for the particular id name in the program. Now it has found the tag which is having the same id name as mentioned. By using the depth-first pre-order traversal of the document the matching of the first element is done. The element in the example which contains the id name as Selector is paragraph tag. The querySelector() method fetches the paragraph tag and applies the particular changes to the content according to the code mentioned.

Uses of jQuery querySelector

Below are the two points explain the uses of querySelector:

The codes of jQuery are more precise, shorter and simpler than the standard JavaScript codes. It can perform a variety of functions.

The call to querySelector() returns the first element as it is picking one, so it is faster and also shorter to write.

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This is a guide to jQuery querySelector. Here we discuss what is jQuery querySelector, introduction to querySelector, syntax and the example of Jquery by using type. You can also go through our other related articles to learn more –

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