Improving Multidimensional Sequential Pattern Mining Algorithm

Computer Science and Engineering

Data mining is an influential technology to help companies to extract hidden and significant predictive information from massive collection of data. In present time, it has become very extensive field for research. In data mining, Frequent pattern mining is receiving increasing attention amongst researchers. It is important and elementary technique used to find frequent patterns. It is having numerous algorithms to find frequent items from given database. Sequential pattern mining, which finds the set of frequent subsequences in sequence databases, is an important data mining task and has broad applications. Usually, sequence patterns are associated with different circumstances, and such circumstances form a multiple dimensional space. For example, customer purchase sequences are associated with region, time, customer group, and others. It is interesting and useful to mine sequential patterns associated with multi-dimensional information. we propose the theme of multi-dimensional sequential pattern mining, which integrates the multidimensional analysis and sequential data mining. We also thoroughly explore efficient methods for multi-dimensional sequential pattern mining. We examine feasible combinations of efficient sequential pattern mining and multidimensional analysis methods, as well as develop uniform methods for high-performance mining.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.