Cluster analysis is a statistical method that groups objects into clusters so that items within a cluster are more similar to each other than to items in other clusters.<\/p>\n\n\n\n
It sits inside exploratory data analysis, which means researchers use it to spot trends and validate hypotheses in data they have already collected, rather than to prove a theory from scratch. The method shows up across pattern recognition, machine learning, market research, data cleaning, and bioinformatics. Because the underlying goal is the same everywhere: find groups that behave differently, without deciding those groups in advance.<\/p>\n\n\n\n
A common example is a bank using transaction data to cluster claims by pattern. It helps investigators flag likely fraud and understand how honest customers behave differently from fraudulent ones.<\/p>\n\n\n\n
How is cluster analysis different from market segmentation?<\/strong><\/h2>\n\n\n\nCluster analysis is the statistical technique used to find groups in data, while market segmentation<\/a> is the business practice of dividing customers into target groups. It often uses cluster analysis as one of its tools. Research firm Ipsos<\/a> makes this same distinction, noting that clustering always produces a grouping, but that grouping only becomes a useful segmentation once it is statistically tested and profiled against real business goals.<\/p>\n\n\n\nSegmentation can also rely on simple, predefined rules, such as splitting customers by age bracket or region. Customer segmentation<\/a> built on cluster analysis instead lets the data reveal groupings that a predefined rule would miss, such as a segment defined by a combination of purchase frequency, price sensitivity, and support ticket history that no single demographic field would capture on its own.<\/p>\n\n\n\nWhat are the main cluster analysis methods?<\/strong><\/h2>\n\n\n\nThe four main cluster analysis methods are hierarchical clustering, centroid-based clustering, distribution-based clustering, and density-based clustering. Each groups data differently and fits different kinds of problems.<\/p>\n\n\n\n![\"\"\/](\"https:\/\/www.questionpro.com\/blog\/wp-content\/uploads\/2022\/07\/cluster-analysis-methods.jpg\")
<\/figure>\n\n\n\n1. Hierarchical clustering<\/h3>\n\n\n\n
Hierarchical clustering, also called connectivity-based clustering, groups data points that share similar characteristics, then links those groups to other clusters with related properties. The core idea is that objects closer together are more related than objects farther apart.<\/p>\n\n\n\n
A divisive version works in reverse, starting with one large data set and splitting it into progressively smaller clusters based on defined linkage criteria. There is no single correct way to partition the data in this method. It makes it flexible but also more subjective to interpret.<\/p>\n\n\n\n
2. Centroid-based clustering<\/h3>\n\n\n\n
Centroid-based clustering defines each cluster by a single central point, most commonly using the k-means algorithm. Data points are grouped around the center that minimizes the distance between them and that centroid.<\/p>\n\n\n\n
The main drawback is that the number of clusters, or k, has to be set in advance. This limits how the data gets represented if that initial number turns out to be wrong.<\/p>\n\n\n\n
3. Distribution-based clustering<\/h3>\n\n\n\n
Distribution-based clustering groups data into clusters based on the statistical distribution the data points appear to follow. It relies on random sampling to build sample sets from a distribution and works best when a business needs to show a correlation between specific attributes and objects.<\/p>\n\n\n\n
The tradeoff is that objects get grouped according to predefined attributes, which can introduce bias if the real data does not actually fit the assumed distribution.<\/p>\n\n\n\n
4. Density-based clustering<\/h3>\n\n\n\n
Density-based clustering, most commonly run through the DBSCAN algorithm, defines clusters based on how densely packed data points are relative to the rest of the data set. Points in sparse areas are treated as noise or border points, since they typically separate one cluster from another.<\/p>\n\n\n\n
The limitation here is that the method needs a noticeable drop in density to mark where one cluster ends and another begins, which can feel arbitrary in data sets with gradual, rather than sharp, transitions.<\/p>\n\n\n\n
How do you choose the right cluster analysis method?<\/strong><\/h2>\n\n\n\nChoosing a clustering method depends on how much you already know about your data’s structure. Since there is no universally correct choice, it is usually decided experimentally unless a clear mathematical reason points to one method.<\/p>\n\n\n\n
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Segmentation can also rely on simple, predefined rules, such as splitting customers by age bracket or region. Customer segmentation<\/a> built on cluster analysis instead lets the data reveal groupings that a predefined rule would miss, such as a segment defined by a combination of purchase frequency, price sensitivity, and support ticket history that no single demographic field would capture on its own.<\/p>\n\n\n\n The four main cluster analysis methods are hierarchical clustering, centroid-based clustering, distribution-based clustering, and density-based clustering. Each groups data differently and fits different kinds of problems.<\/p>\n\n\n\n Hierarchical clustering, also called connectivity-based clustering, groups data points that share similar characteristics, then links those groups to other clusters with related properties. The core idea is that objects closer together are more related than objects farther apart.<\/p>\n\n\n\n A divisive version works in reverse, starting with one large data set and splitting it into progressively smaller clusters based on defined linkage criteria. There is no single correct way to partition the data in this method. It makes it flexible but also more subjective to interpret.<\/p>\n\n\n\n Centroid-based clustering defines each cluster by a single central point, most commonly using the k-means algorithm. Data points are grouped around the center that minimizes the distance between them and that centroid.<\/p>\n\n\n\n The main drawback is that the number of clusters, or k, has to be set in advance. This limits how the data gets represented if that initial number turns out to be wrong.<\/p>\n\n\n\n Distribution-based clustering groups data into clusters based on the statistical distribution the data points appear to follow. It relies on random sampling to build sample sets from a distribution and works best when a business needs to show a correlation between specific attributes and objects.<\/p>\n\n\n\n The tradeoff is that objects get grouped according to predefined attributes, which can introduce bias if the real data does not actually fit the assumed distribution.<\/p>\n\n\n\n Density-based clustering, most commonly run through the DBSCAN algorithm, defines clusters based on how densely packed data points are relative to the rest of the data set. Points in sparse areas are treated as noise or border points, since they typically separate one cluster from another.<\/p>\n\n\n\n The limitation here is that the method needs a noticeable drop in density to mark where one cluster ends and another begins, which can feel arbitrary in data sets with gradual, rather than sharp, transitions.<\/p>\n\n\n\n Choosing a clustering method depends on how much you already know about your data’s structure. Since there is no universally correct choice, it is usually decided experimentally unless a clear mathematical reason points to one method.<\/p>\n\n\n\nWhat are the main cluster analysis methods?<\/strong><\/h2>\n\n\n\n
<\/figure>\n\n\n\n1. Hierarchical clustering<\/h3>\n\n\n\n
2. Centroid-based clustering<\/h3>\n\n\n\n
3. Distribution-based clustering<\/h3>\n\n\n\n
4. Density-based clustering<\/h3>\n\n\n\n
How do you choose the right cluster analysis method?<\/strong><\/h2>\n\n\n\n