In recent years there has been a growing interest in clustering methods stemming from the spectral decomposition of the data affinity matrix, which are shown to present good results on a wide variety of situations. However, a complete theoretical understanding of these methods in terms of data distributions is not yet well understood. In this paper, we propose a spectral clustering based mode m...

Mean shift is a nonparametric clustering technique that does not require the number of clusters in input and can find clusters of arbitrary shapes. While appealing, the performance of the mean shift algorithm is sensitive to the selection of the bandwidth, and can fail to capture the correct clustering structure when multiple modes exist in one cluster. DBSCAN is an efficient density based clus...

Penggunaan trafik internet dalam suatu kantor pemerintah perlu diawasi secara cermat untuk memperoleh efisiensi pemakaiannya baik dan tepat guna. Jalur yang telah disediakan merupakan fasilitas resmi dibiayai dari anggaran bersumber rakyat sehingga cermat. Domain Name System (DNS) menyediakan data kaya menarik, serta dapat diekstraksi mengungkap informasi bisa dianalisis bagi berbagai keperluan...

We propose a novel Mean-Shift method for data clustering, called Robust (RMS). A new update equation point iterates is proposed, mixing the ones of standard (MS) and Blurring (BMS). Despite its simplicity, proposed has not been studied so far. RMS can be set up in both kernel-based nearest-neighbor (NN)-based fashion. Since rule closer to BMS, convergence conjectured based on Chen’s BMS theorem...