Sequential pattern mining is an important data mining problem with broadapplications. However, it is also a challenging problem since the mining may have to generate orexamine a combinatorially explosive number of int...Sequential pattern mining is an important data mining problem with broadapplications. However, it is also a challenging problem since the mining may have to generate orexamine a combinatorially explosive number of intermediate subsequences. Recent studies havedeveloped two major classes of sequential pattern mining methods: (1) a candidategeneration-and-test approach, represented by (ⅰ) GSP, a horizontal format-based sequential patternmining method, and (ⅱ) SPADE, a vertical format-based method; and (2) a pattern-growth method,represented by PrefixSpan and its further extensions, such as gSpan for mining structured patterns.In this study, we perform a systematic introduction and presentation of the pattern-growthmethodology and study its principles and extensions. We first introduce two interestingpattern-growth algorithms, FreeSpan and PrefixSpan, for efficient sequential pattern mining. Then weintroduce gSpan for mining structured patterns using the same methodology. Their relativeperformance in large databases is presented and analyzed. Several extensions of these methods arealso discussed in the paper, including mining multi-level, multi-dimensional patterns and miningconstraint-based patterns.展开更多
文摘Sequential pattern mining is an important data mining problem with broadapplications. However, it is also a challenging problem since the mining may have to generate orexamine a combinatorially explosive number of intermediate subsequences. Recent studies havedeveloped two major classes of sequential pattern mining methods: (1) a candidategeneration-and-test approach, represented by (ⅰ) GSP, a horizontal format-based sequential patternmining method, and (ⅱ) SPADE, a vertical format-based method; and (2) a pattern-growth method,represented by PrefixSpan and its further extensions, such as gSpan for mining structured patterns.In this study, we perform a systematic introduction and presentation of the pattern-growthmethodology and study its principles and extensions. We first introduce two interestingpattern-growth algorithms, FreeSpan and PrefixSpan, for efficient sequential pattern mining. Then weintroduce gSpan for mining structured patterns using the same methodology. Their relativeperformance in large databases is presented and analyzed. Several extensions of these methods arealso discussed in the paper, including mining multi-level, multi-dimensional patterns and miningconstraint-based patterns.
