{'fraction_non_alphanumeric': 0.021798365122615803, 'fraction_numerical': 0.0, 'mean_word_length': 5.282051282051282, '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}}
Extending K-Means to Preserve Spatial Connectivity

Clustering is one of the most important steps in the data processing pipeline. Of all the clustering techniques, perhaps the most widely used technique is K-Means. However, K-Means does not necessarily result in clusters which are spatially connected and hence the technique remains unusable for several remote sensing, geoscience and geographic information science (GISci) data. In this article, we propose an extension of K-Means algorithm which results in spatially connected clusters. We empirically verify that this indeed is true and use the proposed algorithm to obtain most significant group of waterbodies mapped from multispectral image acquired by IRS LISS-III satellite.