The K-means algorithm is a popular data-clustering algorithm. However, one of its drawbacks is the requirement for the number of clusters, K, to be specified before the algorithm is applied. This paper first reviews existing methods for selecting the number of clusters for the algorithm. Factors that affect this selection are then discussed and a new measure to assist the selection is proposed....

Clustering of objects is an important area of research and application in variety of fields. In this paper we present a good technique for data clustering and application of this Technique for data clustering in a closed area. We compare this method with K-nearest neighbor and K-means.  

Penyakit jantung adalah kondisi dimana sebagai organ vital manusia mengalami gangguan dan tidak berfungsi dengan baik merupakan penyakit yang paling mematikan di dunia serta menjadi penyebab utama kematian secara global, total sekitar 17,9 juta jiwa per tahunnya. Pada penelitian ini dilakukan pengelompokkan data pasien terdiagnosis untuk melihat karakteristik persamaan dari setiap pasien. Datas...

The k-means algorithm is often used in clustering applications but its usage requires a complete data matrix. Missing data, however, common many applications. Mainstream approaches to missing reduce the problem formulation through either deletion or imputation these solutions may incur significant costs. Our k-POD method presents simple extension of for that works even when missingness mechanis...

Multiple kernel clustering aims to seek an appropriate combination of base kernels mine inherent non-linear information for optimal clustering. Late fusion algorithms generate partitions independently and integrate them in the following procedure, improving overall efficiency. However, separate partition generation leads inadequate negotiation with procedure a great loss beneficial correspondin...