A Brief Guide to top 5 Clustering Methods in Data Mining

This entry was posted in Research Paper , on October 30 , 2019.

Research involves working with vast amounts of data (structured & unstructured). Although structured data can be assessed without grouping them, unstructured data must be segregated into logical groups prior to analyzing them. This is exactly where the clustering methods is used. Clustering methods, also known as the data segmentation method, is the process of grouping specific data depending on their similarities and characteristics. In data mining, this approach segregates the data (using a special join algorithm) that are best suited for data analysis. The clustering process allows an element to be a part of a cluster. This is known as soft clustering. However, in hard clustering, the element is not allowed to be a part of the cluster. To effectively perform the clustering, several factors should be taken into account. 

  • Scalability – Highly scalable clustering algorithms are required to manage large databases. 
  • High & low dimensionality – The clustering algorithm must be able to handle both low and high dimensional space. 
  • Interpretability – The clustering outcomes must be comprehensible, interpretable and usable. 
  • Determination of clusters with attribute shape – The algorithm must be able to discover clusters of arbitrary shape and must not be bounded only to the distance measures. 
  • Ability to handle various types of attributes – Clustering algorithm must be flexible enough to be applied to any kind of data including categorical, numerical and binary data. 
Based on the cluster models, different types of clustering methods can be applied to the set of data. However, the choice of the type of clustering solely depends on the features of the data set. 

Centroid-based Clustering method 

In this type of clustering, every cluster is referenced by the vector of values. Here, each element is the part of the cluster whose difference value is minimum when compared to other clusters. This clustering method is close to classification subjects and can be used to solve optimization issues. However, the drawback of the centroid-based method is that it requires the clusters to be pre-defined. 

Density-based Clustering method 

This type of clustering algorithm is based on the high density of data participants. That is, the clusters are formed according to the high density of the participants of a set of data in a given location. This is achieved by aggregating some distance notions to standard density levels to group the participants in clusters. The major drawback of the density-based approach is it does not perform well in determining the limit areas of the group. 

Grid-based Clustering method 

As the name suggests, the elements form a grid. This type of clustering method utilizes a multiresolution grid data structure. Grid-based clustering technique takes space-driven method by partitioning the embedding spaces into cells that are independent of input objects distribution. It differs from the conventional type of clustering as it deals with the value space and not data points. The advantage of the grid-based method is it can be applied to any attribute type and offers flexibility pertaining to the level of granularity. 

Partitioning-based Clustering method 

This type of clustering method segregates the data into several subsets. For instance, if a database of ‘n’ elements is given. The partition method then creates ‘k’ partition data. Here, each partition is represented as a cluster and ask ≤ n. This implies that the data is classified into ‘k’ groups, which then satisfies requirements such as (A)inclusion of at least one element in a group and (B) affiliation of each element to only one group. Some of the points to consider while using this clustering method are: (a)  for a given number of partitions, the partitioning approach will develop initial partitioning. (b) it uses iterative relocation strategy to enhance the partitioning by moving elements from one group to another.

Hierarchical-based Clustering method   

This type of clustering develops a hierarchical decomposition of given data sets. Based on the hierarchical decomposition, this type of clustering is divided into agglomerative and divisive clustering approach. An agglomerative method, also known as a bottom-up approach, begins with each element forming a separate group. The elements or groups keep merging thereafter. The divisive approach, called a top-down method, begins with all elements in the same group. The group/cluster is then split into smaller clusters in the continuous iteration. Clustering algorithms, an essential factor of data mining, finds its application in several areas, including pattern recognition, market research, image processing, categorize genes, and many more.