{'fraction_non_alphanumeric': 0.012470771628994544, 'fraction_numerical': 0.0, 'mean_word_length': 5.388059701492537, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A genetic algorithm based ensemble approach for categorical data clustering

In this paper, we propose a genetic algorithm based procedure to combine different clustering solutions obtained for the same data set to construct a relatively good solution. Clustering ensemble technique, alternatively also known as clustering aggregation or consensus clustering, considers the different individual solutions obtained and combines them into a single solution of better quality using a consensus function. In the genetic algorithm based consensus function proposed here, each cluster is represented using a single representative. A chromosome represents a set of cluster representatives for a particular clustering result. Single point crossover is made between the two cluster representatives of highest similarity value so that changes in the clusters of the selected pair of chromosomes are small i.e. the clustering solutions gradually converge to the optimal one. Mutation is performed in such a way that the properties of cluster representatives remain invariant. A new fitness measure is proposed to evaluate the fitness of the cluster representatives as well as of the individual chromosomes. Experiments are made on real life state-of the -art data sets and results are reported.