Unique Metric for Health Analysis with Optimization of Clustering Activity and Cross Comparison of Results from Different Approach

TL;DR

This paper introduces a novel metric for evaluating cluster health, optimizing clustering activity, and enabling cross-method comparison, which aids in selecting the appropriate number of clusters and reducing noise.

Contribution

The paper presents a new method to assess cluster quality, compare different clustering approaches, and optimize clustering results by eliminating noisy variables.

Findings

The proposed metric effectively identifies optimal cluster numbers.

It enables comparison of clustering methods on the same dataset.

The technique improves clustering accuracy by noise elimination.

Abstract

In machine learning and data mining, Cluster analysis is one of the most widely used unsupervised learning technique. Philosophy of this algorithm is to find similar data items and group them together based on any distance function in multidimensional space. These methods are suitable for finding groups of data that behave in a coherent fashion. The perspective may vary for clustering i.e. the way we want to find similarity, some methods are based on distance such as K-Means technique and some are probability based, like GMM. Understanding prominent segment of data is always challenging as multidimension space does not allow us to have a look and feel of the distance or any visual context on the health of the clustering. While explaining data using clusters, the major problem is to tell how many cluster are good enough to explain the data. Generally basic descriptive statistics are used to estimate cluster behaviour like scree plot, dendrogram etc. We propose a novel method to understand the cluster behaviour which can be used not only to find right number of clusters but can also be used to access the difference of health between different clustering methods on same data. Our technique would also help to also eliminate the noisy variables and optimize the clustering result. keywords - Clustering, Metric, K-means, hierarchical clustering, silhoutte, clustering index, measures