Improving Clustering Method Performance Using K-Means, Mini batch K-Means, BIRCH and Spectral

Wahyuningrum, Tenia and Khomsah, Siti and Suyanto, suyanto and Meliana, Selly and Eko Yunanto, Prasti and F. Al Maki, Wikky (2021) Improving Clustering Method Performance Using K-Means, Mini batch K-Means, BIRCH and Spectral. In: International Seminar on Research of Information Technology and Intelligent Systems (ISRITI).

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Abstract

The most pressing problem of the k-Nearest Neighbor (KNN) classification method is voting technology, which will lead to poor accuracy of some randomly distributed complex data sets. To overcome the weaknesses of KNN, we developed a new scheme in data set clustering, making the number of clusters greater than the number of data classes. In addition, the committee selects each cluster so that it does not use voting techniques such as standard KNN methods. This study uses two sequential methods, namely the clustering method and the KNN method. Clustering methods can be used to group records into multiple clusters to select commissions from these clusters. Five clustering methods were tested: KMeans, K-Means with Principal Component Analysis (PCA), Mini Batch K-Means, Spectral and Balanced Iterative Reduction, and Clustering using Hierarchies (BIRCH). All tested clustering methods are based on the cluster type of the center of gravity. According to the result, the BIRCH method has the lowest failure rate among the five clustering methods (2.13), and K-Means has the largest clusters (156.63).

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