Cluster Analysis

In large heterogeneous or disorganized sets, some elements may turn out to be similar to each other in various ways. Cluster analysis is a statistical model of data analysis, where, in a disorganized set, elements with similar characters are grouped into clusters. Various algorithms and methods are used to cluster data into a particular cluster or group. Cluster analysis is a great method to observe distinct features of each cluster and gain further insights from them. 

For a rough example, we can consider all the motor vehicles used in the capital of India as a dataset. The dataset can be broken down into clusters of cars, motorcycles, trucks, and buses. This is a classic example of cluster analysis, where we can use each of the clusters for further study and understanding. 

Using Cluster Analysis in Data Mining

Data mining is a very common use of the cluster analysis technique. It is a great means to find objects that are similar to each other in a group. This would help analysts and scientists work in the particular field and make their work several times easy.

Clustering helps in identifying people with a particular preference and thus gain a valuable member for a particular dataset. There are some popular clustering methods for data mining, which are as follows:

1. Partitioning Clustering Method: In this method, all data members are initially grouped, and they eventually separate using continuous iterations.
2. Hierarchical Clustering Method: This method separates all the members initially, and they keep on merging to form a cluster until a specific termination condition is met, or all members are clustered.
3. Density-Based Clustering Method: This method is based on the density of the data members. The cluster grows continuously, with at least one member in the radius of the group.
4. Grid-Based Clustering Method: A grid-like structure is formed where the object space is quantified into a finite number of cells
5. Model-Based Clustering Methods: Here, every cluster is hypothesized to find the most suitable data for the model. 
6. Constraint-Based Clustering Method: The clustering is performed with the application or user-oriented constraints that group data. 

Understand Better with This Cluster Analysis Example

To understand the concept of cluster analysis better, let us we can consider a dataset consisting of all the pets people of India have. In this set, we can make smaller groups or clusters of animals like dogs, cats, fish, birds, and many more. Further, from the cluster of dogs or fish, we can gain greater insights into their breed and owner preferences among others. 

Another example can be a dataset of countries in this world. They can be grouped or divided into clusters based on the continent they are from. 

Types of Data in Cluster Analysis

A dataset meant for cluster analysis has to contain data in a particular form. The various types of data in cluster analysis are described briefly below. 

1. Interval-Scaled Variables: They are continuous measurements with just about a linear scale. 
2. Binary Variables: These variables can hold only two values, either male or female
3. Nominal or Categorical Variables: They are like the general form of binary variables, and can contain more than 2 values.
4. Ordinal Variables: These variables can either be discrete or continuous
5. Ratio-scaled variable: These variables hold a positive value on the non-linear scale, approximately at an exponential scale. 
6. Variables Of Mixed Type: These datasets may contain all of the six types of variables – symmetric and asymmetric binary, ordinal, interval,  nominal, and ratio.

Conclusion

Hence, after understanding the concept and theory behind cluster analysis, you must surely try it out yourself. Self-practice would be a great way to become accustomed to the method and also understand the advantages of cluster analysis. You can use sample data sets that are available online for practicing the cluster analysis method of interpreting data. Trying out different data analysis methods would be beneficial for understanding the subtle differences between them. Further, knowing their application would let you make a better decision when thinking of which method to use on a particular dataset.