# initKmix -- A Novel Initial Partition Generation Algorithm for Clustering Mixed Data using k-means-based Clustering

## TL;DR

This paper introduces initKmix, an innovative initialization method for k-means clustering on mixed datasets, which improves accuracy and consistency over traditional random initializations by aggregating multiple clustering runs.

## Contribution

The paper presents initKmix, a new initialization algorithm that enhances k-means clustering for mixed data by combining results from multiple runs based on different attribute initializations.

## Key findings

- initKmix outperforms random initialization in accuracy and consistency
- initKmix achieves results comparable or superior to state-of-the-art methods
- Experiments confirm initKmix's effectiveness on various datasets

## Abstract

Mixed datasets consist of both numeric and categorical attributes. Various k-means-based clustering algorithms have been developed for these datasets. Generally, these algorithms use random partition as a starting point, which tends to produce different clustering results for different runs. In this paper, we propose, initKmix, a novel algorithm for finding an initial partition for k-means-based clustering algorithms for mixed datasets. In the initKmix algorithm, a k-means-based clustering algorithm is run many times, and in each run, one of the attributes is used to create initial clusters for that run. The clustering results of various runs are combined to produce the initial partition. This initial partition is then used as a seed to a k-means-based clustering algorithm to cluster mixed data. Experiments with various categorical and mixed datasets showed that initKmix produced accurate and consistent results, and outperformed the random initial partition method and other state-of-the-art initialization methods. Experiments also showed that k-means-based clustering for mixed datasets with initKmix performed similar to or better than many state-of-the-art clustering algorithms for categorical and mixed datasets.