Cluster analysis groups data points so that records in the same group are more alike than records in different groups, without using any labels. The main types are partitioning methods such as k-means, hierarchical methods, and density-based methods such as DBSCAN.

Every result rests on four decisions: which variables go in and how they are scaled, how “alike” is measured, which algorithm gathers the groups, and how many groups you ask for. Check the number of clusters with more than one signal, such as the elbow plot and the silhouette score, then ask whether you can name each group.

I ran the same k-means on the same 400 customers twice. Same data. Same number of clusters. The first run gave me groups of 184, 97 and 119 customers. The second gave me 65, 281 and 54. Neither run was a bug. The only difference was one line of code that put every column on the same scale. I will show you exactly what happened further down, because it explains most of what goes wrong in cluster analysis. If you want guided practice on real datasets, Imarticus Learning’s data analytics course is a sensible place to build the Python and statistics base.   

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See How Different Cluster Analysis Methods Work

The same dataset can produce very different groupings depending on the clustering method you choose. K-means looks for central points, DBSCAN identifies dense regions, and hierarchical clustering builds a tree of relationships. Use the interactive demo below to change the settings, compare the results, and see how each method approaches the same task.

What Is Cluster Analysis?

Cluster analysis is a method that groups data points so that items in the same group are more alike than items in other groups. It is unsupervised, so you give the algorithm no labels. The groups, called clusters, come from the data itself. If a colleague asked me to define cluster analysis in one line, I would say it finds groups in data that nobody has labelled. Everything else is detail.

Cluster Analysis Meaning In Simple Terms

Picture a scatter of dots on a page. You can see the blobs without any help. Now picture the same dots spread across 30 columns instead of two. You cannot see anything. A computer still can. That is the cluster analysis meaning in practice: teaching a machine to see the blobs you would see, in dimensions you cannot draw. It needs two things.

• A way to measure how alike two records are
• A rule for gathering the alike ones. Every method in this post is a different answer to those two problems.

I think of any result as the product of four decisions. What goes in, meaning the variables and how they are scaled. How “alike” is measured. Which algorithm gathers the groups. How many groups you ask for. Change one and the clusters can move. I will keep calling these the four decisions.

Cluster Analysis In Statistics

Statisticians were here first, and they use different words for much of it. They usually cluster cases, such as people or shops, on their scores across several variables. They can also cluster the variables themselves, which comes close to factor analysis.

Cluster analysis in statistics and in machine learning starts without them. And the measure of “alike” you choose depends on the types of data in cluster analysis you are holding, which is the next section.

What Is Cluster Analysis In Data Mining?

If you have ever searched for what is cluster analysis in data mining and got back a wall of formulas, this is the plain version. Cluster analysis in data mining is where I would start with almost any unlabelled table, because it asks for no hypothesis and no training examples. You get a first map of the data. The map tells you what to ask next.

In data mining, cluster analysis is an unsupervised technique that sorts records into groups based on similarity, with no predefined classes. Analysts use it to explore a new dataset, summarise large tables, spot outliers and prepare data for other models.

You will often hear that clustering has no right answer because there are no labels. True, and a bit of a cop-out. No answer key doesn’t mean no standard. My standard is simple:

• Can someone name each group in a sentence
• Would they do something different for each one?

Four tidy clusters that fail that test are a failed analysis, however neat the maths looks. A purely technical way to define cluster analysis leaves that out.

The last row is what makes clustering hard. With classification you can mark your own homework. With clustering you cannot, so judgement carries more weight than most tutorials admit. A good method should cope with large tables, mixed variable types, groups that are not round, and a little noise. None does all four well. That is why there are so many.  

A data analyst’s role goes well beyond preparing reports or working with spreadsheets. Understanding this day-to-day work gives you a clearer picture of what the role demands in a real workplace.

Types Of Data In Cluster Analysis

Before I choose an algorithm, I look at the columns. The types of data in cluster analysis in data mining decide which distance measure I can trust, and a wrong measure quietly wrecks everything after it.

Numbers: Interval-Scaled And Ratio-Scaled Data

Interval-scaled data is numbers on an even scale, like age, temperature or order value. Euclidean or Manhattan distance works, but only after scaling. Skip that, and the column with the biggest numbers wins every argument. That is exactly what happened in my opening example.

Ratio-scaled data has a true zero and tends to grow multiplicatively, such as visit counts or bacteria counts. A log transform often tames it. After that, you can treat it like interval data.

Categories: Binary, Nominal And Ordinal Data

Binary data has two outcomes, like bought or did not buy. Watch the zeros. If “did not buy” is true for nearly everyone, two customers who both say no have told you almost nothing. Jaccard similarity ignores shared zeros for exactly that reason.

Nominal data are categories with no order, such as city or payment method. Simple matching counts the categories two records share. K-modes swaps the average for the most common category. One-hot encoding works too, though it can bloat a table fast.

Ordinal data is ordered categories, like a rating from 1 to 5. The gaps between steps are not guaranteed to be equal, so I would rescale the ranks to a 0 to 1 range before measuring distance.

Mixed Data

Real tables mix everything. Gower distance, introduced by J. C. Gower in 1971, measures each column with a rule that suits its type and averages the results. K-prototypes does a similar job inside a k-means-style algorithm.

This table maps the types of data in cluster analysis in data mining to the measures and algorithms that usually fit.

If your table has numbers, categories and ratings side by side, give the distance measure a full afternoon. It is the decision people rush.    

Pro Tip: Do not settle on three or four clusters just because the output looks tidy. Compare plausible solutions using silhouette scores, cluster sizes and whether the groups make sense for the problem. 

Types Of Cluster Analysis

Every clustering method asks a different question of your data.

• K-means asks: where are the centres?
• Hierarchical methods ask: who is closest to whom?
• DBSCAN asks: where is it crowded?
• Gaussian mixture models ask: which overlapping bell curves explain this?

Pick the question that matches your data and the rest follows. The table below lines up the main types of cluster analysis against the things that decide which one you use.

Partitioning methods in cluster analysis make you pick k first. Hierarchical and density methods let you skip that, but they hand you a different decision instead: where to cut the tree, or how to set eps. You never get out of choosing.    

Partitioning Methods In Cluster Analysis

Partitioning methods split the data into k groups, and you choose k. The name k-means comes from James MacQueen, who used it in a 1967 paper. The idea is almost embarrassingly simple.

• Pick k starting centres.
• Assign every point to its nearest centre.
• Move each centre to the average of its points.
• Repeat until nothing moves.

Take six dots on a grid: (1, 2), (1, 4), (2, 3), (8, 8), (9, 10) and (10, 9). Your eye sees two groups. Start with centre A at (1, 2) and centre B at (8, 8), and measure the straight-line distance from every dot to each centre.

Now move each centre to the average of its dots. A lands at (1.33, 3.00) and B at (9.00, 9.00). Assign again. Nothing changes, so the centres stay put and the algorithm stops. Two passes. Real data takes more, but the logic does not change.

K-medoids does the same job with a real data point as the centre instead of an average. That makes it steadier around outliers. It also gets slow on big tables. The case for k-means is strong. K-means is quick; it also scales to large tables with very little fuss, and everyone understands it. I still think it gets picked out of habit.

Hierarchical Cluster Analysis

Hierarchical cluster analysis is what I reach for when I do not trust my own guess for k. It builds a tree instead of asking for a number.

• Agglomerative methods start with every point alone and keep merging the two closest groups.
• Divisive methods run the other way. The tree is called a dendrogram, and you cut it at a height that gives you a sensible number of groups.

“Closest” needs a definition once groups hold more than one point. That is the linkage choice, and it changes the result. Single linkage uses the two nearest points and tends to string groups into chains.

• Complete linkage uses the two furthest points and gives tight, similar-sized groups.
• Average linkage takes the mean across all pairs.
• Ward’s method merges whichever pair adds the least variance.

If I had to pick blind, I would start with Ward and compare it against average linkage. Two drawbacks. A merge cannot be undone, so an early poor call stays in the tree. And on big tables, the number of rows and columns matter more than you would like, because the method compares many pairs. Hierarchical cluster analysis is a tool for small and medium tables, and for exploring. It is not for a million customers.

Density-Based Clustering

What does DBSCAN need? Two settings. Eps is how close two points must be to count as neighbours. Min_samples is how many neighbours make a region dense enough to start a cluster. Everything outside a dense region is labelled noise instead of being forced into a group. Martin Ester and colleagues introduced it in 1996.

I think that noise label is the most underrated idea in clustering. Most methods insist every record belongs somewhere. DBSCAN is allowed to say “none of the above”. The scikit-learn documentation warns that eps matters a great deal: too small and most of the data goes unclustered, too large and separate groups merge. It also struggles when real groups have very different densities.

Grid-Based, Model-Based And Fuzzy Clustering

Three more, briefly. Grid-based methods cut the space into cells and cluster the dense ones, which is fast on very large data. Model-based methods assume the data comes from a mix of probability distributions, usually Gaussian, and give each point a probability of belonging to each group. Fuzzy clustering, such as fuzzy c-means, lets one point sit in several clusters to different degrees, which helps when groups overlap.    

Cluster Analysis Diagram: How To Read One

Caveat before the pictures. The data in all three figures is simulated: 400 made-up customers, with three groups that I built in. Real data is messier, and I do not want a tidy chart to mislead you.

1. A cluster analysis diagram of 400 simulated customers, coloured by k-means cluster. Each dot is one customer.

Read it by asking three questions. What are the axes? Here, days since last purchase and total spend. What do the colours mean? The cluster each customer landed in. And where do the colours touch? Those borderline customers are the ones I would look at first. One limit. We clustered on three variables, and a flat chart shows two. Purchase count is hidden. A cluster analysis diagram is a picture of one run, not the truth.

2. A dendrogram of 30 sampled customers using Ward linkage.

A dendrogram reads from the bottom up. Each join is a merge, and the height of the join shows how far apart the groups were when they merged. A long vertical gap means the groups were far apart, so cutting across a long gap gives well-separated clusters. My dashed line sits at 70 per cent of the tallest merge. That is a convenient choice, nothing more.

3. An elbow plot. The curve bends at three clusters.

The big drop comes between two and three clusters. After three, extra clusters buy very little. That bend is a first hint for k, and nothing more. In my view, the elbow is the most over-trusted chart in data science. On messy real data, the bend can be a gentle curve, and two people will see two different elbows. 

Cluster Analysis Tools

Cluster analysis tools help you group similar data points, uncover hidden patterns, and make sense of large datasets without assigning categories in advance. Tools such as Python’s scikit-learn, R, Excel and specialist platforms like KNIME support methods such as K-means, hierarchical clustering and DBSCAN. The right choice depends on your dataset size, the complexity of the patterns you want to find, and how much control you need over the analysis. 

• Python is the most common choice, with scikit-learn and SciPy covering k-means, hierarchical methods and DBSCAN.
• R has kmeans and hclust built in.
• SPSS suits people who prefer menus, and KNIME offers a drag-and-drop route with no code.
• In Excel, you are limited to hand-built formulas or an add-in.

One newer direction is worth watching. Text embeddings, which are lists of numbers that represent the meaning of a document, can be clustered with the same methods to group documents by topic. The tools change. The four decisions do not.  

Moving into data science and AI does not always require a technical background. What matters is building the right foundations, from working with data and statistics to applying machine learning and AI tools to practical problems. The learning curve becomes more manageable when concepts are introduced step by step and reinforced through hands-on projects.

How To Perform Cluster Analysis Step By Step

Plenty of pages answer what is cluster analysis in data mining. Far fewer show the workflow around the algorithm, and the workflow is where the work is. These seven steps are the four decisions with some housework around them.

1. Write down the decision the groups will feed. “Segment customers for the March email campaign” is a question. “Find clusters” is not.
2. Choose the variables. Pick columns that relate to the question and note the type of each. Mixed types of data in cluster analysis need extra care in step 4.
3. Clean and scale. Fill or drop missing values, look at outliers, then standardise. A distance-based method cannot know that a rupee and a day are different things.
4. Pick a distance measure. Check which of the types of data in cluster analysis in data mining you are holding, then match the measure using the table above.
5. Pick an algorithm. Start with a simple one. Partitioning methods in cluster analysis, k-means first among them, suit big and roughly round data. Hierarchical methods suit exploring, and DBSCAN suits odd shapes and noise.
6. Choose the number of clusters. Try a range, read the elbow plot and the silhouette scores, then ask whether you can describe each group in a sentence. Often you end up with fewer clusters than your first guess, and that is fine.
7. Validate and profile. Rerun with another random seed. If the groups fall apart, they were never solid. Then summarise each cluster by its averages and give it a name.

Can you do this in Excel? Standard Excel has no one-click clustering command. People build k-means by hand with formulas, use an add-in such as XLSTAT, or use Python inside Excel where their Microsoft 365 plan allows it. Past a few hundred rows, I would use Python or R.  

Cluster Analysis Example: Customer Segmentation

Caveat first, because I would want it from anyone showing me results. This data is simulated, built to look like the customers of a small online shop. There are 400 rows and three numbers per row: days since the last purchase, number of purchases, and total spend in rupees. I put three groups in on purpose, so I know what a good answer looks like. You will not have that luxury on real data.

First question: how many groups? Inertia, the within-cluster sum of squares, fell from 371.3 at two clusters to 147.7 at three, then only to 122.4 at four. That bend at three is the elbow.

Silhouette scores were 0.635 for two clusters, 0.641 for three and 0.544 for four. Look at that first pair. Silhouette barely separated two from three, and the elbow did the deciding. On real data, I would want a third signal, plus a check that the groups make business sense. So I went with three.

The cluster numbers are arbitrary labels. Naming them is your job, and so is deciding what to do about each one. Reward the loyal group, nudge the steady middle, and win back the drifting customers with a clear offer.

Now back to the opening. The second run was the same k-means with three clusters on the raw numbers, with no scaling. Group sizes came out at 65, 281 and 54, against 184, 97 and 119 with scaling.

Spend is measured in thousands of rupees and recency in tens of days, so spend dominated every distance. Same data, same k, different answer. Scaling is not decoration. For the dendrogram in Figure 2 I tried hierarchical cluster analysis on a sample of just 30 customers. All 400 would have made an unreadable chart.   

Did You Know?
Some clustering algorithms can leave data points unassigned to any cluster. DBSCAN labels points in low-density regions as noise instead of forcing every observation into a group. This can be useful when a dataset contains outliers or naturally separated dense regions. (Source)   

Where Is Cluster Analysis Used?

Wherever there are many records and no ready-made categories, cluster analysis in data mining projects tend to appear. Marketing gets the attention. I find the quieter uses more interesting.

Psychology papers lean on cluster analysis in statistics packages such as SPSS to group patients by symptom profile. Every cluster analysis example above has the same shape: many records, no labels, and a decision waiting on the groups.  

Also Read: How Long Does a Data Analyst Course Take to Complete? 

How To Validate Cluster Analysis Results

There is no answer key, so I check four things, and I trust the set more than any single one.

• The elbow. Look for the point where extra clusters stop tightening the groups much.
• The silhouette score. It ranges from -1 to 1. Values near 1 mean points sit well inside their own cluster and far from the others. Peter Rousseeuw introduced it in 1987, and the scikit-learn silhouette documentation explains the calculation.
• Stability. Rerun with different random seeds, or on a random subset. If the groups fall apart, they were not solid.
• Usefulness. Can you name each cluster in a sentence, and does each one suggest a different action?

Then write your assumptions next to the result: variables, scaling, distance, algorithm and k. Those are the four decisions. Someone who changes one may get different clusters, and they should know why. Put simply, a cluster result with its decisions missing is half a result.  

Advantages, Limitations And Common Mistakes

The upside is real.

• You need no labels
• You can find groups nobody thought to look for
• The methods scale from a spreadsheet to millions of rows.

Now the limits.

• You usually choose the number of clusters yourself
• The algorithm will hand you that many even when the data holds no real groups.
• Results move with scaling, distance and starting points, so one run proves little.
• K-means expects roughly round groups and chops curved ones into pieces. Outliers drag its centres around.

Cluster analysis in statistics has a quiet weakness: it will find groups in random noise. The algorithm always returns something. It’s easy to fall for a tidy chart and stop checking. The mistakes I would watch for are the boring ones.

• Skipping scaling.
• Choosing k to match what you hoped to see.
• Clustering on every column because it is there.

Treating the output as a fact instead of a view. Never rerunning it. I would rather see four variables you can defend than twelve you cannot. 

Also Read: How Do You Handle Missing Values During Data Preprocessing?   

How Cluster Analysis Moves From Theory To Practice With Imarticus Learning

Knowing how K-means works is one thing. Using it to group customers, interpret the patterns and explain what a business can do with those findings is a different skill. That practical step is built into Imarticus Learning’s data analytics course. Its machine learning curriculum covers K-means clustering, hierarchical clustering and RFM analysis, with a dedicated project on customer segmentation using K-means. The course also offers:

• 35+ tools and projects to build practical experience across data science and analytics.
• Advanced Data Science and AI internships where learners can tackle real-world challenges.
• A job-ready project portfolio to demonstrate their work, rather than relying on course completion alone.
• GenAI training that includes using AI tools for code generation, debugging and statistical analysis.
• 10 guaranteed interviews with companies, as advertised on the course page. Review the programme’s terms to understand the eligibility and conditions.
• NSDC certification on successful completion, as stated on the programme page.
• A choice of learning format: classroom or live online, with six-month weekday and ten-month weekend options.

For someone building a career in analytics, the customer segmentation project is the most relevant starting point. It connects the concept discussed in this article to a task you may need to explain in a project review or job interview.

FAQs About Cluster Analysis

From choosing the right clustering method to interpreting results and handling outliers, these answers clear up frequently asked questions about cluster analysis and its practical uses. 

What is an example of cluster analysis?

Let us consider the following example of Cluster Analysis: A retailer grouping customers by how recently, how often and how much they buy is a standard cluster analysis example. The result might be loyal big spenders, a steady middle and lapsed buyers, each getting a different marketing approach.

What are the three types of cluster analysis?

The three most common types of cluster analysis are partitioning (such as k-means), hierarchical (agglomerative or divisive) and density-based (such as DBSCAN). Grid-based, model-based and fuzzy methods also exist, so three is a convenient grouping in cluster analysis, not a fixed rule.

How can I perform cluster analysis in Excel?

You cannot perform cluster analysis in Excel directly, since there is no one-click clustering command in Excel. However, you can do so, by building k-means by hand with formulas, use an add-in such as XLSTAT, or run Python inside Excel where your plan allows. For bigger cluster analysis work, Python is easier, and Imarticus Learning’s data analytics course is one place to learn it.

What is cluster analysis in marketing?

In marketing, cluster analysis groups customers by behaviour, spend or preferences, so each group gets its own message, offer or product. It turns one large customer list into a few usable segments. Imarticus Learning’s data analytics course is one way for marketers to pick up the skills.

What is cluster analysis and its types?

Cluster analysis groups similar records without using labels. Its main types are partitioning, hierarchical, density-based, grid-based and model-based methods, plus fuzzy clustering. Each suits different data: k-means for compact groups, DBSCAN for odd shapes, hierarchical when you want a tree.

How to apply cluster analysis?

To apply cluster analysis, define the question, choose variables, clean and scale them, pick a distance measure and an algorithm, choose the number of clusters, then validate and profile the groups. Imarticus Learning’s data analytics course offers structured Python practice for this.

Where is cluster analysis used?

Cluster analysis is used in marketing segmentation, finance risk grouping and fraud checks, healthcare, biology and botany, psychology, education, image processing and real estate. Anyone building an analytics career, including through Imarticus Learning, will meet it often.

What is the main goal of cluster analysis?

The main goal of cluster analysis is to find natural groups in data, so records in a group are similar and records in different groups differ. It turns a large unlabelled table into a small set of segments you can understand and act on.   

Build Practical Skills in Cluster Analysis 

Cluster analysis is useful when a dataset contains patterns that are difficult to spot by looking at individual records. It can help you group customers by buying behaviour, identify similar research subjects or find recurring patterns in operational data. The value lies in what those groups reveal, not in how many clusters an algorithm produces.

There is one check worth keeping in mind: a cluster is a statistical grouping, not an explanation in itself. You still need to examine the characteristics of each group, test whether the patterns hold up and decide whether they make sense in the real world.

For students looking to build a career in analytics, this is where practical work matters. Working with tools such as Python, SQL and machine learning methods can help you move from knowing how clustering works to applying it to business problems. Imarticus Learning’s Data Analytics Course includes K-means and hierarchical clustering, alongside a customer segmentation project using K-means. It is one option to consider if you want structured training that connects analytical concepts with project work.

Ultimately, good cluster analysis does more than sort data into groups. It helps you see patterns clearly, question your assumptions and make decisions based on evidence rather than guesswork.

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