A MULTI-CRITERIA DECISION MAKING BASED METHOD FOR RANKING SEQUENTIAL PATTERNS
Z Dashti, Mir Mohsen Pedram, J. Shanbehzadeh · 2010
Abstract — Sequences are one of the most important types of data. Recently, mining and analysis of sequence data has been studied in several fields. Sequence database mining and change mining is an example of data mining to study temporal data. Specific changes might be important to decision maker in different time periods to schedule future activities. Working with long sequences requires useful method. This paper presents a study on similarity measure and ranking sequence data. We employed sequence distance function based on structural features to measure the similarity, and a multi-criteria decision making techniques to rank them. Index Terms — Sequences similarity; distance function; conditional probability distribution; multi-criteria decision making; TOPSIS. I.