Maximal Prevalent Co-location Patterns

Abstract

A novel maximal co-location pattern mining framework based on maximal cliques and hash tables (MCHT) is developed in this chapter. First, all maximal cliques that can compactly represent neighbor relationships between instances of a spatial data set are enumerated. The advantages of bit string operations are fully utilized to speed up the process of enumerating maximal cliques. Next, a participating instance hash table structure is constructed for these maximal cliques. Then information about maximal patterns can be queried and collected efficiently from the hash table. After that, by calculating participation indexes of these patterns to measure their prevalence, maximal prevalent co-location patterns can be filtered efficiently. Finally, a series of experiments are conducted on both synthetic and real-facility data sets to demonstrate that the proposed algorithm can efficiently reduce both the computational time and memory consumption compared with the existing algorithms.