TY - GEN
T1 - Mining Segment-Wise Periodic Patterns in Time-Related Databases
AU - Han, Jiawei
AU - Gong, Wan
AU - Yin, Yiwen
N1 - Periodicity search, that is, search for cyclic patterns in time-related data sets, is an important data mining problem with many applications. Most previously stud-led methods on periodicity pattern search are on mining full-cycle periodicity in the sense that every point in the period contribute to the part of the cycle, such as all the days in the year contribute (approximately) to the season cycles of the year. However, there exists another kind of periodicity, which we call segment-wise period-icily in the sense that only some of the segments in a time sequence have cyclic behavior. For example, Laura may read Vancouver Sun at 7:00 to 7:30 every weekday morning but may do all sorts of things afterwards; Company W’s stock may rise almost every Wednesday but could be unpredictable at other time slots (see Figure 1); and Jack may work regularly (full-cycle periodicity) during working hours but he can only be found at 9:00-10:00 every Mondaym orning (segment-wise periodicity). These examples show that segment-wise peri- The research was supported in part by the research grants from the Natural Sciences and Engineering Research Council of Canada, Networkso f Centres of Excellent Programo f Canada, MPRT eltech Ltd., and B.C. Advanced Systems Institute. Copyright @1998A, mericanA ssociation for Artificial Intelligence (www.aaai.org).A ll rights reserved.
PY - 1998
Y1 - 1998
N2 - Periodicity search, that is, search for cychcity in time-related databases, is an interesting data mining problem. Most previous studies have been on finding full-cycle periodicity for aü the segments in the selected sequences of the data, that is, if a sequence is periodic, all the points or segments in the period repeat. However, it is often useful to mine segment-wise or point-wise periodicity in (ime-related data sets. In this study, we integrate data cube and Apriori data mining techniques for mining segment-wise periodicity in regard to a fixed length period and show that data cube provides an efficient structure and a convenient way for interactive mining of multiple-level periodicity.
AB - Periodicity search, that is, search for cychcity in time-related databases, is an interesting data mining problem. Most previous studies have been on finding full-cycle periodicity for aü the segments in the selected sequences of the data, that is, if a sequence is periodic, all the points or segments in the period repeat. However, it is often useful to mine segment-wise or point-wise periodicity in (ime-related data sets. In this study, we integrate data cube and Apriori data mining techniques for mining segment-wise periodicity in regard to a fixed length period and show that data cube provides an efficient structure and a convenient way for interactive mining of multiple-level periodicity.
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M3 - Conference contribution
AN - SCOPUS:70449107982
T3 - Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, KDD 1998
SP - 214
EP - 218
BT - Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, KDD 1998
PB - American Association for Artificial Intelligence (AAAI) Press
T2 - 4th International Conference on Knowledge Discovery and Data Mining, KDD 1998
Y2 - 27 August 1998 through 31 August 1998
ER -