Abstract
Given a data sequence, sequential pattern mining, which finds frequent sequence patterns among them, is an important data mining problem. However, in the existing sequential pattern mining, only the purchase order of the items is considered, and the position where the item is purchased is not considered. In this paper, we developed a sequential pattern mining algorithm using Apache spark. The proposed algorithm finds frequent sequential patterns in parallel by distributing data to several machines. Experimentally, we performed a comprehensive performance study on the proposed algorithm by varying various parameter values using various synthetic data. Experimental results show that the proposed algorithm shows a linear speed improvement over the number of machines.
Highlights
The development of IT technology and the computer and internet industries has increased the need to handle large amounts of data in modern society
We propose location-based sequential pattern mining algorithm based on PrefixSpan to handle location data
EXPERIMENTS we evaluate the performance of our proposed two sequential pattern mining algorithms, Naïve Location-based PrefixSpan (NLPS), and the MapReduce Location-based PrefixSpan (MRLPS)
Summary
The development of IT technology and the computer and internet industries has increased the need to handle large amounts of data in modern society. We have developed a sequential pattern mining algorithm that considers the purchase location using MapReduce programming model on Hadoop distributed environment. Problem: Given a database that contains m location-based sequences and a specified minimum support δ, the problem is to find all set of sequential patterns in the database. Most of them were sequential pattern candidates generated by apriori-style method This approach had to tally a set of many candidate sequence patterns, and had to scan the database multiple times to find long-length sequential patterns. To solve this problem, PrefixSpan (Prefix-projected Sequential pattern mining) algorithm has been proposed. The support count of β in α-projected database D|r, denoted as supportD|r (β), is the number of sequences γ in D|r
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