A Weighted Majority Voting based on NMI for Cluster Analysis
نویسندگان
چکیده
Due to advancements in data acquisition, large amount of data are collected in daily basis. Analysis of the collected data is an important task to discover the patterns, extract the features, and make informed decisions. A vital step in data analysis is dividing the objects (elements, individuals) in different groups based on their similarities. One way to group the objects is clustering. Clustering methods can be divided in two categories, linear and non-linear. K-means is a commonly used linear clustering method, while Kernel K-means is a non-linear technique. Kernel K-means projects the elements to a new space using a kernel function and then group them in different clusters. Different kernels perform differently when they are applied to different data sets. Choosing the right kernel for an application could be challenging, however applying a set of kernels and aggregating the results could provide a robust performance for different data sets. In this work, we address this issue and propose a weighted majority voting to ensemble the results of three different kernels.
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