Clustering After detecting clusters, a business can develop

Clustering is a broad set of techniques for finding subgroups of observations within a data set. When we cluster observations, we want observations in the same group to be similar and observations in different groups to be dissimilar. Because there isn’t a response variable, this is an unsupervised method, which implies that it seeks to find relationships between the n observations without being trained by a response variable. Clustering allows us to identify which observations are alike, and potentially categorize them therein.
. Clusters have the following properties:
• We find them during the operation and their number is also not always fixed in advance.
• They are the combination of objects having similar characteristics.
Clustering is one of the most widespread descriptive methods of data analysis and data mining. We use it when data volume is large to find homogeneous subsets that we can process and analyze in different ways.
For example, a food product manufacturing company can categorize its customers on the basis of purchased items and cost of those items.
Applications of Clustering
Following are the main Clustering applications:
• Marketing – In this field, clustering is useful in finding customer profiles that make customer base. After detecting clusters, a business can develop a specific strategy for each cluster base. We can use clusters to keep track of customers over months and detect a number of customers who moved from 1 cluster to other.
• Retail – In the retail industry, we use clustering to divide all stores of a particular company into groups of establishments on basis of type of customer, turnover etc.
• Medical Science – In medical, we use clustering discover a group of patients suitable for particular treatment protocols. Each group comprises all patients who react in the same way. Formation of these groups is on basis of age, type of disease etc. We can also us clustering in the classification of the protein sequence, CT-scans etc.
• Sociology – We use Clustering in performing data mining operations here. We divide the population into groups of individuals who are homogeneous in terms of social demographics, lifestyle, expectations etc. We can then use the categorization for purposes like polls, identifying criminals etc.


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