Comparing N-Node Set Importance Representative results with Node Importance Representative results for Categorical Clustering: An exploratory study

TL;DR

This paper compares two algorithms, N-Node Importance Representative and Node Importance Representative, for clustering large categorical datasets, proposing a new method that improves data point allocation accuracy.

Contribution

It analyzes and contrasts NIR and NNIR algorithms, revealing their contradictions, and introduces a novel approach for better data point resemblance assessment in clustering.

Findings

NIR and NNIR algorithms contradict each other in data resemblance.

The new method improves data point allocation accuracy.

Enhanced clustering efficiency for large categorical datasets.

Abstract

The proportionate increase in the size of the data with increase in space implies that clustering a very large data set becomes difficult and is a time consuming process.Sampling is one important technique to scale down the size of dataset and to improve the efficiency of clustering. After sampling allocating unlabeled objects into proper clusters is impossible in the categorical domain.To address the problem, Chen employed a method called MAximal Representative Data Labeling to allocate each unlabeled data point to the appropriate cluster based on Node Importance Representative and N-Node Importance Representative algorithms. This paper took off from Chen s investigation and analyzed and compared the results of NIR and NNIR leading to the conclusion that the two processes contradict each other when it comes to finding the resemblance between an unlabeled data point and a cluster.A new and better way of solving the problem was arrived at that finds resemblance between unlabeled data point within all clusters, while also providing maximal resemblance for allocation of data in the required cluster.