Clustering and classifying are overriding techniques in machine learning. Neighborhood
construction as a key step in these techniques has been extensively used for modeling local …

We propose a novel method for template matching in unconstrained environments. Its
essence is the Best-Buddies Similarity (BBS), a useful, robust, and parameter-free similarity …

Most of the existing clustering methods have difficulty in processing complex nonlinear data
sets. To remedy this deficiency, in this paper, a novel data model termed Hybrid K-Nearest …

In this paper, we address challenges of detecting instances from emerging classes over a
non-stationary data stream during data classification. In particular, data instances from an …

Neighborhood-based and density-based clustering methods are applied in various data
analysis applications. However, most of them have low performance in mixed …

It is expected for new clustering algorithms to find the appropriate number of clusters when
dealing with complex data, meaning various shapes and densities. They also have to be self …

The density peaks clustering (DPC) is a clustering method proposed by Rodriguez and Laio
(Science, 2014), which sets up a decision graph to identify the cluster centers of data points …

With the rapid rate of information flow today, local identification of similar data points has
gained greater significance for information processing in various branches of sciences …

The clustering algorithm based on mutual K-nearest neighbors (MKNN) is presented to
identify coherent structures in complicated fluid flows, in order to analyze the mass mixing …

Most real world networks like social networks, protein-protein interaction networks, etc. can
be represented as graphs which tend to include densely connected subgroups or modules …