Behaviour Similarity Using Formal Concept Analysis

Clustering depends critically on density and distance (similarity), but these concepts become increasingly more difficult to define as dimensionality increases. Similarity measure that is based on the number of neighbours that two points share, and define the density of a point as the sum of the similarities of a point’s nearest neighbours. Cluster analysis is a challenging task and there are a number of well-known issues associated with it, e.g., finding clusters in data where there are clusters of different shapes, sizes, and density or where the data has lots of noise and outliers. In this paper, formal concept analysis used to build lattice and then find the similar objects by calculating experimentally using similarity measures.