Cluster Analysis In Data Mining: Meaning, Application, Requirement And Clustering Methods

Cluster Analysis In Data Mining: Meaning, Application, Requirement And Clustering Methods

In this blog, we will discuss cluster analysis in data mining. So, before this, let us know what is clustering in data mining, what important points to consider, what are its requirements, and methods.

What is clustering in data mining

In clustering, a group of diverse data objects is categorised as similar objects. One group is referred to as a cluster of data. All the given data sets are distributed into different groups in the cluster analysis based on the similarity of the data. After the classification of data has been done into small groups, they are assigned a label. It helps in accepting changes by doing classification.

This process of making a group of abstract objects into classes of similar objects is referred to as clustering in data mining.

Important Points To Remember In Clustering

• One group is referred to as a cluster of data objects.
• When the cluster analysis is done, the first step is to divide the data sets into groups using data similarity, after which the groups are assigned to their respective labels.
• The biggest benefit of clustering over-classification is that it helps in singling out useful features that differentiate different groups.

Applications of cluster analysis 

• Cluster Analytics Services is widely popular and used in many applications like in data analysis, image processing, and pattern recognition.
• It allows marketers to collect customer data into different groups and characterize their customer groups by using purchasing patterns.
• It is used in the biology field to derive animal and plant taxonomies, discovering genes with similar potentials.
• It also helps in identifying information by classifying all data documents on the web.

What are the Requirements That Clustering in Data Mining Should satisfy?

The main requirements that a clustering algorithm must have are:

• Interpretability and Usability

The clustering results should be usable, comprehensible and interpretable. Grouping can help in giving structured data by organizing it into similar data objects. It becomes comfortable for a data expert in processing and learning new things. 

• High Dimensionality

Data clustering can handle both high dimensional data as well as data of small size.

• Discovering clusters with arbitrary shapes

Arbitrary shape clusters are used by the clustering algorithm. Small size clusters can also be seen with spherical shapes.

• Dealing with different types of attributes

Many different types of data can be used with clustering algorithms. The data can be of any type such as binary, categorical and interval-based data.

• Scalability

The database is quite big to deal with. It must be scalable to handle an extensive database, to make it scalable.

Data Mining Clustering Methods

The clustering methods can be categorized into the following types:

1. Partitioning Clustering Method

In this method, a cluster is represented by each partition. For instance, the 'n' partition is done on a database object 'a’. A cluster will be defined under each partition and represented by each partition and n < a is the number of groups that is done after the classification of objects. When partition clustering is done, it must satisfy two conditions, such as:

• An object should be assigned to only one group.
• Every group must have a purpose.​

2. Hierarchical Clustering Methods

In this hierarchical clustering approach, the data set is created based on hierarchical decomposition. On this basis, the purpose of classification will be decided. There are two types of approaches for hierarchical decomposition creation, which are:

• Divisive Approach
• Agglomerative Approach​

3. Density-Based Clustering Method

In this method of clustering in data mining, the main focus is on density. The mass notion is used in the clustering method. When this method is done, the cluster keeps growing. One number of points for each data point must be there at least in the radius of the group.

4. Grid-Based Clustering Method

In this type of Clustering Method, a grid is created employing the object together. A grid structure is prepared using the object space in a limited number of cells.

5. Model-Based Clustering Methods

In model-based clustering, every cluster is hypothesized to find the most suited data for the model. The density function discovers the group in this method. 

6. Constraint-Based Clustering Method

Application or user-oriented rules are included to complete the clustering. The user expectation is known as a constraint. When the expectation of the user is referred to as the constraint. The grouping process makes communication very interactive, which is furnished by the restrictions.