Clustering in Data Mining – Algorithms of Cluster Analysis in Data Mining

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In this blog, we will study Cluster Analysis in Data Mining. First, we will study clustering in data mining and the introduction and requirements of clustering in Data mining.

Moreover, we will discuss the applications & algorithm of Cluster Analysis in Data Mining. Further, we will cover Data Mining Clustering Methods and approaches to Cluster Analysis.

So, let’s start exploring Clustering in Data Mining.

Introduction to Cluster Analysis

a. What is Clustering in Data Mining?

  • Generally, a group of abstract objects into classes of similar objects is made.
  • We treat a cluster of data objects as one group.
  • While doing cluster analysis, we first partition the set of data into groups. That based on data similarity and then assign the labels to the groups.
  • The main advantage of over-classification is that it is adaptable to changes. And helps single out useful features that distinguish different groups.

b. What is Cluster Analysis in Data Mining?

Finding groups of objects such that the objects in a group will be like one another. And different from the objects in other groups.

Applications of Data Mining Cluster Analysis

  • Data Clustering analysis is used in many applications. Such as market research, pattern recognition, data analysis, and image processing.
  • Data Clustering can also help marketers discover distinct groups in their customer base. And they can characterize their customer groups based on the purchasing patterns.
  • In the field of biology, it can be used to derive plant and animal taxonomies. categorize genes with similar functionalities and gain insight into structures inherent to populations.
  • Clustering in Data Mining helps in identification of areas. That is of similar land use in an earth observation database. It also helps in the identification of groups of houses in a city. That is according to house type, value, and geographic location.
  • Clustering in Data Mining also helps in classifying documents on the web for information discovery
  • Also, we use Data clustering in outlier detection applications. Such as detection of credit card fraud.
  • As a data mining function, cluster analysis serves as a tool. That is to gain insight into the distribution of data. Also, need to observe characteristics of each cluster.

Requirements of Clustering in Data Mining

The following points state us the requirement of clustering in Data Mining:

a. Scalability

We need highly scalable clustering algorithms to deal with large databases.

b. Ability to deal with different kinds of attributes

Algorithms should be capable to be applied to any kind of data. Such as interval-based data, categorical, and binary data.

c. Discovery of clusters with attribute shape

The clustering algorithm should be capable of detecting clusters of arbitrary shape. They should not be bounded by only distance measures. That tends to find a spherical cluster of small sizes.

d. High dimensionality

The clustering algorithm should not only be able to handle low-dimensional data. Although, need to handle the high dimensional space.

e. Ability to deal with noisy data

Databases contain noisy, missing or erroneous data. Some algorithms are sensitive to such data and may lead to poor quality clusters.

f. Interpretability

The clustering results should be interpretable, comprehensible, and usable.

Data Mining Clustering Methods

Data Mining Clustering Methods are classified into the following categories −
Clustering in Data Mining - Clustering Methods

Clustering in Data Mining – Clustering Methods

a. Partitioning Clustering Method

Suppose we are given a database of ‘n’ objects. And the partitioning method constructs ‘k’ partition of data. Each partition will represent a cluster and k ≤ n. It means that it will classify the data into k groups. That must need to satisfy the following requirements −
  • Each group contains at least one object.
  • Each object must belong to exactly one group.
Points to remember −
  • If we have a given number of partitions (say k). Then the partitioning method will create an initial partitioning.
  • Further, it uses the iterative relocation technique. That is to improve the partitioning by moving objects from one group to other.

b. Hierarchical Clustering Methods

The hierarchical method creates a hierarchical decomposition of the given set of data objects. We can classify methods on the basis of how the hierarchical decomposition is formed. There are two approaches here −
  • Agglomerative Approach
  • Divisive Approach
i. Agglomerative Approach
This approach is also known as the bottom-up approach. In this, we start with each object forming a separate group. It keeps on merging the objects or groups that are close to one another.
It keeps on doing so until all of the groups are merged into one or until the termination condition holds. 
ii. Divisive Approach
This approach is also known as the top-down approach. In this, we start with all of the objects in the same cluster. Then, in the continuous iteration, a cluster is split up into smaller clusters. Also, it is down until each object in one cluster or the termination condition holds.
Hence, this method is rigid, i.e., once a merging or splitting is done, it can never be undone.
Approaches to Improve Quality of Hierarchical Clustering in Data Mining
Here are the two approaches. That we used to improve the quality of hierarchical clustering in Data Mining−
  • Perform careful analysis of object linkages at each hierarchical partitioning.
  • Integrate hierarchical agglomeration by using a hierarchical agglomerative algorithm. Then to group objects into micro-clusters, and then performing macro-clustering on the micro-clusters.

c. Density-Based Clustering Method

This Data Mining Clustering method is based on the notion of density. The idea is to continue growing the given cluster. That is exceeding as long as the density in the neighbourhood threshold.
For each data point within a given cluster, the radius of a given cluster has to contain at least number of points.

d. Grid-Based Clustering Method

In this, the objects together form a grid. The object space is quantized into a finite number of cells that form a grid structure.
  • The major advantage of this method is a fast processing time.
  • It is dependent only on the number of cells in each dimension in the quantized space.

e. Model-Based Clustering Methods

In this Data Mining Clustering method, a model is hypothesized for each cluster to find the best fit of data for a given model. Also, this method locates the clusters by clustering the density function.
Thus, it reflects the spatial distribution of the data points.
This method also provides a way to determine the number of clusters. That was based on standard statistics, taking outlier or noise into account. It, therefore, yields robust clustering methods.

f. Constraint-Based Clustering Method

The clustering is performed by the incorporation of a user or application-oriented constraints. A constraint refers to the user expectation. Constraints provide us with an interactive way of communication with the clustering process. Constraints can be specified by the user or the application need.

What is Not Cluster Analysis?

  • Supervised classification – Have class label information
  • Simple segmentation – Dividing students into different registration groups, by the last name
  • Results of a query – Basically, groupings are a result of an external specification
  • Graph partitioning – Some mutual relevance and synergy, but areas are not identical

So, this was all about Clustering in Data Mining. Hope you like our explanation.


As a result, we have studied introduction to clustering in Data Mining. Also, learned about Data Mining Clustering methods and approaches to Cluster Analysis in Data Mining. Furthermore, if you feel any query, feel free to ask in a comment section.

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1 Response

  1. Anusha reddy says:

    The information was too good

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