initKmix -- A Novel Initial Partition Generation Algorithm for Clustering Mixed Data using k-means-based Clustering
This addresses the issue of unreliable clustering for researchers and practitioners working with mixed datasets, though it is incremental as it builds on existing k-means-based approaches.
The paper tackles the problem of inconsistent clustering results from random initialization in k-means-based algorithms for mixed data by proposing initKmix, a method that generates an initial partition by combining runs using different attributes, resulting in more accurate and consistent outcomes that outperform random and other state-of-the-art methods.
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.