Clustering is the process of grouping data points together so that points within a group are more similar to each other than points outside of the group. Clustering is a common technique used in text analytics, where it is often used to group together documents with similar content. Clustering can also be used for non-text data, such as grouping together customers with similar purchasing behaviors.
Disadvantages of Using Clustering
A disadvantage of using clustering is that it can be difficult to interpret the results. Clusters may not be easily identifiable, and it can be difficult to determine how many clusters should be created. Additionally, clustering can be sensitive to outliers, which can impact the results.
Tools Used to Perform Clustering
There are a variety of tools that can be used to perform clustering, including Excel, R, and Python.
Clustering vs. Classification
It is important to note that clustering is different from classification. In classification, data points are assigned to pre-defined groups, whereas in clustering, the groups are formed based on the similarity of the data points. For example, a document could be classified as “sports” or “non-sports”, whereas it would be clustered with other documents that are similar in content.
Comparing Clustering to Other Terms
Clustering is often confused with other terms, such as segmentation and grouping. Segmentation is the process of dividing a population into groups, usually based on demographic criteria such as age, gender, or income. Grouping is the process of creating groups of data points that are similar to each other. Clustering is similar to grouping, but the groups are not necessarily created based on similarity. For example, a dataset could be grouped by year, but the groups would not necessarily be similar to each other.