Kolmogorov Complexity: Clustering Objects and Similarity

TL;DR

This paper introduces a similarity measure based on Kolmogorov Complexity for clustering objects like web pages, demonstrating its potential to utilize web features such as hit counts for Indonesian intellectual content.

Contribution

It proposes a novel similarity measure derived from Kolmogorov Complexity specifically for clustering web-based objects.

Findings

The Kolmogorov Complexity-based similarity measure is feasible for web object clustering.

Web features like hit counts can be effectively exploited using the proposed method.

The approach shows promise for clustering Indonesian intellectual web content.

Abstract

The clustering objects has become one of themes in many studies, and do not few researchers use the similarity to cluster the instances automatically. However, few research consider using Kommogorov Complexity to get information about objects from documents, such as Web pages, where the rich information from an approach proved to be difficult to. In this paper, we proposed a similarity measure from Kolmogorov Complexity, and we demonstrate the possibility of exploiting features from Web based on hit counts for objects of Indonesia Intellectual.