Witnessing the tremendous development of machine learning technology, emerging applications impose challenges using domain knowledge to improve accuracy clustering provided that suffers a compromising rate despite its advantage fast procession. In this paper, we model (i.e., background or side information), respecting some as must-link and cannot-link sets, for sake collaborating with k-means b...

COVID-19 hits the world like a storm by arising pandemic situations for most of countries around world. The whole is trying to overcome this situation. A better health care quality may help country tackle pandemic. Making clusters with similar types provides an insight into in different countries. In area machine learning and data science, K-means clustering algorithm typically used create base...

Abstract This paper performs an investigation of Kleinberg’s axioms (from both intuitive and formal standpoint) as they relate to the well-known k -mean clustering method. The axioms, well a novel variations thereof, are analyzed in Euclidean space. A few natural properties proposed, resulting -means satisfying intuition behind (or, rather, small, variation on that intuition). In particular, tw...

Designing tools that support group formation is a challenging goal for both the areas of adaptive and collaborative e-learning environments. Group formation may be used for a variety of purposes such as for grouping students that could potentially benefit from cooperation based on their individual characteristics or needs, for mediating peer help by matching peer learners, for facilitating inst...

k-means++ [5] seeding procedure is a simple sampling based algorithm that is used to quickly find k centers which may then be used to start the Lloyd’s method. There has been some progress recently on understanding this sampling algorithm. Ostrovsky et al. [10] showed that if the data satisfies the separation condition that ∆k−1(P ) ∆k(P ) ≥ c (∆i(P ) is the optimal cost w.r.t. i centers, c > 1...