Fast Search Algorithm for Determining the Optimal Number of Clusters using Cluster Validity Index

Determining the Optimal Number of Clusters in Cluster Analysis

Cluster analysis is the multivariate method which objective is to classify the objects. In current literature there are many methods and many distances measures, which can be mutually combined. There is no manual and rule which would clearly identify the appropriate combination method and distance measures during clustering. Simultaneously, in cluster analysis it is often necessary to determine...

Determining Number of Clusters Using Firefly Algorithm with Cluster Merging for Text Clustering

Text mining, in particular the clustering is mostly used by search engines to increase the recall and precision of a search query. The content of online websites (text, blogs, chats, news, etc.) are dynamically updated, never‐ theless relevant information on the changes made are not present. Such a scenario requires a dynamic text clustering method that operates without initial knowledge on a d...

OptCluster : an R package for determining the optimal clustering algorithm and optimal number of clusters

Validity Index and number of clusters

Clustering (or cluster analysis) has been used widely in pattern recognition, image processing, and data analysis. It aims to organize a collection of data items into c clusters, such that items within a cluster are more similar to each other than they are items in the other clusters. The number of clusters c is the most important parameter, in the sense that the remaining parameters have less ...

A Novel Validity Index for Determination of the Optimal Number of Clusters

The structural characteristics of clusters are investigated in the partitioning process. Two partition functions, which show opposite properties around the optimal cluster number, are found and a new cluster validity index is presented based on the combination of these functions. Some properties of the index function are discussed and numerical examples are presented. key words: clustering, val...