Hierarchical clustering methods have attracted much attention by giving the user a maximum amount of flexibility. Rather than requiring parameter choices to be predetermined, the result represents all possible levels of granularity. In this paper a hierarchical method is introduced that is fundamentally related to partitioning methods, such as k-medoids and k-means as well as to a density based...

This paper considers the problem of representative selection: choosing a subset of data points from a dataset that best represents its overall set of elements. This subset needs to inherently reflect the type of information contained in the entire set, while minimizing redundancy. For such purposes, clustering may seem like a natural approach. However, existing clustering methods are not ideall...

In this work we analyze data for 314 participants of a methadone study over 180 days. Dosages in mg were converted for better interpretability to seven categories in which six categories have an ordinal scale for representing dosages and one category for missing dosages. We develop a dissimilarity measure and cluster the time series using “partitioning around medoids” (PAM). The dissimilarity m...

We introduce the problem of identifying representative nodes in probabilistic graphs, motivated by the need to produce different simple views to large networks. We define a probabilistic similarity measure for nodes, and then apply clustering methods to find groups of nodes. Finally, a representative is output from each cluster. We report on experiments with real biomedical data, using both the...


 BPJS Ketenagakerjaan bertugas menyelenggarakan program jaminan sosial bagi para pekerja di Indonesia, seperti Jaminan Kecelakaan Kerja, Hari Tua, Pensiun, Kematian, dan Pemeliharaan Kesehatan. Pengelompokan bukan penerima upah dapat menggunakan metode clustering. Dalam penelitian ini, peneliti membandingkan dua algoritma clustering yaitu K-Means K-Medoids untuk mengelompokkan berdasarkan...

This paper is used to cluster the various components of a bank customer details and segregate potential customers eligible for loan. The application of this technique helps the banker to scale the potentiality of their customers and take necessary steps to decide for loan approval. The classic difficulty of recognizing the customer’s potentiality is solved using this thesis and helps them to ju...

The well known k-medoids clustering approach groups objects through finding k representative objects based on the pairwise (dis)similarities of objects in the data set. In real applications, using only one object to capture or interpret each cluster may not be sufficient enough which in turn could affect the accuracy of the data analysis. In this paper, we propose a new fuzzy clustering approac...

We address in this paper the assisted construction of bilingual thematic comparable corpora by means of co-clustering bilingual documents collected from raw sources such as the Web. The proposed approach is based on a quantitative comparability measure and a co-clustering approach which allow to mix similarity measures existing in each of the two linguistic spaces with a ”thematic” comparabilit...

In this paper, some clustering techniques are analyzed to compare their ability to detect botnet traffic by selecting features that distinguish connections belonging to or not belonging to a botnet. By considering the history of network’s connections, some clustering algorithms are used to derive a set of rules to decide which should be considered as a botnet. Our main contribution is to evalua...