{'fraction_non_alphanumeric': 0.014814814814814815, 'fraction_numerical': 0.0, 'mean_word_length': 5.103225806451613, '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 New Technique For Feature Selection And Cluster Center Initialization

Clustering high dimensional data and initialization of clusters are big challenges in data mining. As the number of dimensions in the data set increase, many traditional clustering techniques begin to suffer because of the curse of dimensionality. Moreover, the results differ with randomly initialized centers of the initial clusters. In this paper, we propose a new algorithm which consists of two phases. In phase one; it uses feature selection technique based on variance to find relevant attributes and then the obtained reduced data set is partitioned into k clusters. Afterwards, it computes Mediod of each of the clusters to obtain initial centers. In phase two, it apply standard k-means algorithm to obtain clusters. It improves efficiency of the clustering process tremendously and our experiment on test data set shows that accuracy of the results has improved