Mining Maximal Generalized Frequent Geographic Patterns with Knowledge Constraints

In frequent geographic pattern mining a large amount of patterns is well known a priori. This paper presents a novel approach for mining frequent geographic patterns without associations that are previously known as noninteresting. Geographic dependences are eliminated during the frequent set generation using prior knowledge. After the dependence elimination maximal generalized frequent sets are computed to remove redundant frequent sets. Experimental results show a significant reduction of both the number of frequent sets and the computational time for mining maximal frequent geographic patterns.

• Type: Conference
• Year: 2006
• Where: ICDM
• Authors: Vania Bogorny, João Francisco Valiati, Sandro da Silva Camargo, Paulo Martins Engel, Bart Kuijpers, Luis Otávio Alvares
• Keywords: Data Mining, Frequent Geographic Patterns, Frequent Sets, ICDM 2006, Redundant Frequent Sets