Improved fuzzy space-intervals based sequential pattern mining: Technical solution

• Published in: 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) pp. 1 - 4
• Main Authors: Nair, Harsha, Neeba, E. A.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.12.2015
• Subjects: Algorithms; Clustering algorithms; Computation; Computational intelligence; Conferences; Data mining; frequent sequential patterns; Fuzzy; fuzzy support; Information technology; Intervals; minimum support; minimum support threshold; Pattern analysis; Proposals; sequential pattern mining; space cluster
• ISBN: 1479978485; 9781479978489
• DOI: 10.1109/ICCIC.2015.7435671

One of the sub areas of the data mining includes sequential pattern mining. This mining algorithm is to find the repeating patterns after mining the sequence databases. These are used to find the relation between the various items in the data for different purposes. As these data keep changing according to the change in time, mining should be done on incremented or updated database to obtain the frequent sequential patterns. The proposed algorithm in this paper uses modified algorithm of sequential pattern mining including concepts of fuzzy space intervals. In this algorithm, frequently occurring sequential patterns in the sequence database are mined using apriori like method. Fuzzy theory is used for mining the space interval between the frequently occurring sequences. The sequentially occurring candidate patterns are found first. After that follows the frequently occurring sequential patterns, which are found by calculating the minimum fuzzy support along with the use of the fuzzy number. Each space cluster is found by fuzzy support computation. The final results comprises the frequently occurring fuzzy space sequentially based patterns. At last the outcome also confirms the excellence of the suggested MISPFSI algorithm.