Wide variety of methods had been designed under the cluster analysis; an unsupervised learning, like partitioning based, hierarchical, density based, model based, etc. DBSCAN, one of the most widely applied density based clustering algorithm outperforms partitioning based clustering algorithms such as k-means, CLARA, CLARANS and hierarchical algorithms, as it does not require a prior knowledge ...

Clustering is the assignment of data objects (records) into groups (called clusters) so that data objects from the same cluster are more similar to each other than objects from different clusters. Clustering techniques have been discussed extensively in similarity search, Segmentation, Statistics ,Machine Learning ,Trend Analysis, Pattern Recognition and classification. Clustering methods can b...

Clustering may be the most fundamental problem in unsupervised learning which is still active in machine learning research because its importance in many applications. Popular methods like K-means, may suffer from instability as they are prone to get stuck in its local minima. Recently, the sum-of-norms (SON) model (also known as clustering path), which is a convex relaxation of hierarchical cl...