Efficient Partial Multiple Periodic Patterns Mining without Redundant Rules

Partial periodic patterns mining is a very interesting domain in data mining problem. In the previous studies, full and partial multiple periodic patterns mining problems are considered. The proposed methods, however, may produce redundant information and are inefficient. In this paper, a novel concept and new parameters are proposed to improve the performance of partial multiple periodic patterns mining. Moreover, the proposed method will not produce redundant information. Without mining every period, we only check the necessary period and use this information to do further mining. Instead of considering the whole database, the information needed for mining partial periodic patterns is transformed into a bit vector which can be stored in a main memory. Therefore, our approach needs to scan the database at most two times. A set of simulations is also performed to show the benefit of our approach. KeywordData Mining, Partial periodicity, Cyclic patterns, Time series analysis.

• Type: Conference
• Year: 2004
• Where: COMPSAC
• Authors: Wenpo Yang, Guanling Lee
• Keywords: COMPSAC 2004, Multiple Periodic Patterns, Partial Periodic Patterns, Periodic Patterns Mining, Software Engineering