Sequential Patterns Mining in Multiple Data Streams

• Main Authors: Wei-ting Chen, 陳威廷
• Other Authors: Ching-Ming Chao
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/35162014108124967289

碩士 === 東吳大學 === 資訊管理學系 === 97 === Sequential patterns mining searches for the relative sequence of events, allowing users to make predictions on discovered sequential patterns. The application of the technique is considerably prevalent among commercial transactions, meteorology and health care…etc. Due to IT progress in recent years, data has changed rapidly, growth in the amount of data explodes and real-time demand increase, leading to a so-called data stream environment. In this environment, data cannot be fully stored and ineptitude in traditional mining techniques has led to the emergence of data streaming mining technology. With application of this mining technology, a database mining which could not store massive amount of data can even provides users with real-time mining results. Multiple data streams are a branch off the data stream environment. In the study of multiple data streams, sequential pattern mining is still one of the many important issues. Nonetheless, the previously proposed MILE algorithm from the study has a limitation to preserving the previous minding sequential pattern when a new data is entered due to the concept of one-time fashion mining. To address this problem, we propose an ICspan algorithm to continue mining sequential patterns through an incremental approach and to acquire a more accurate mining result. In addition, due to the algorithm’s constraint in closed sequential patterns mining, the generation and records for sequential patterns will be reduced, leading to the reduction of memory usage and to effectively increase execution efficiency.