Analysis of Single-View and Multi-view K-Means Clustering on Big Data Environment
Keywords:
Multi-view dataset, Multi-view clustering techniques, K-means, Jaccard Coefficient (Jacc), Fowlkes Mallows Index (FM), Normalized Mutual Information (NMI), Rand Index (RI)Abstract
Due to the revolutionary advancements in the signal sensing devices and its availability to civilians, the real time datasets are now having multiple views. Thus such a multi-view datasets are quite common in era of big data domain. As against learning of single-view, learning of multi-view has plenty of benefits. Clustering has been very useful technique in the machine learning and data mining. Traditional clustering techniques use only single set of features of the available dataset. However for the multi-view dataset with multiple features, how to ensemble all of these data views is a major concern. Thus problem is termed as multi-view clustering problem. The key benefits of multi-view clustering against single-view clustering are accurate description of data, reducing noises of data, and wider range of applications. This research works highlight the impact multi-view K-means clustering available in mvlearn python package with the traditional K-means clustering technique. To assess the impact of simple K-means technique and multi-view version of K-means technique, two datasets are utilized namely, nutrimouse and simulated dataset. In order to analyze the impact of multi-view clustering on clustering quality, traditional k-means technique is applied to individual views, concatenated view of the both the datasets, followed by the application of multi-view version of K-means technique on the both the datasets. We analyzed the clustering quality of multi-view K-means technique using various performance evaluation parameters such as Jaccard Coefficient (Jacc), Fowlkes Mallows Index (FM), Normalized Mutual Information (NMI), Rand Index (RI), and clustering execution times.
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