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Mixed datasets consist of both numeric and categorical attributes. Variousk-means-based clustering algorithms have been developed for these datasets.Generally, these algorithms use random partition as a starting point, whichtends to produce different clustering results for different runs. In thispaper, we propose, initKmix, a novel algorithm for finding an initial partitionfor k-means-based clustering algorithms for mixed datasets. In the initKmixalgorithm, a k-means-based clustering algorithm is run many times, and in eachrun, one of the attributes is used to create initial clusters for that run. Theclustering results of various runs are combined to produce the initialpartition. This initial partition is then used as a seed to a k-means-basedclustering algorithm to cluster mixed data. Experiments with variouscategorical and mixed datasets showed that initKmix produced accurate andconsistent results, and outperformed the random initial partition method andother state-of-the-art initialization methods. Experiments also showed thatk-means-based clustering for mixed datasets with initKmix performed similar toor better than many state-of-the-art clustering algorithms for categorical andmixed datasets.

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