A Novel Algorithm for Informative Meta Similarity Clusters Using Minimum Spanning Tree

TL;DR

This paper introduces two new minimum spanning tree-based clustering algorithms that produce informative, compact, and well-separated clusters, enhancing cluster analysis with guarantees on intra- and inter-cluster similarity.

Contribution

The paper proposes two novel MST-based clustering algorithms using divisive and agglomerative approaches to generate informative meta similarity clusters with guaranteed properties.

Findings

First algorithm produces k clusters with guaranteed intra-cluster similarity.

Second algorithm creates a dendrogram with guaranteed inter-cluster similarity.

Both algorithms identify central and tight clusters effectively.

Abstract

The minimum spanning tree clustering algorithm is capable of detecting clusters with irregular boundaries. In this paper we propose two minimum spanning trees based clustering algorithm. The first algorithm produces k clusters with center and guaranteed intra-cluster similarity. The radius and diameter of k clusters are computed to find the tightness of k clusters. The variance of the k clusters are also computed to find the compactness of the clusters. The second algorithm is proposed to create a dendrogram using the k clusters as objects with guaranteed inter-cluster similarity. The algorithm is also finds central cluster from the k number of clusters. The first algorithm uses divisive approach, where as the second algorithm uses agglomerative approach. In this paper we used both the approaches to find Informative Meta similarity clusters.