An Algorithm to Mine Normalized Weighted Sequential Patterns Using a Prefix-projected Database
Sequential pattern mining is an important subject in data mining with broad applications in many different areas. However, previous sequential mining algorithms mostly aimed to calculate the number of occurrences (the support) without regard to the degree of importance of different data items. In th...
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| Published in | Serdica : journal of computing Vol. 9; no. 2; pp. 105 - 122 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
18.04.2016
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| Online Access | Get full text |
| ISSN | 1312-6555 1314-7897 1314-7897 |
| DOI | 10.55630/sjc.2015.9.105-122 |
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| Summary: | Sequential pattern mining is an important subject in data mining with broad applications in many different areas. However, previous sequential mining algorithms mostly aimed to calculate the number of occurrences (the support) without regard to the degree of importance of different data items. In this paper, we propose to explore the search space of subsequences with normalized weights. We are not only interested in the numberof occurrences of the sequences (supports of sequences), but also concernedabout importance of sequences (weights). When generating subsequencecandidates we use both the support and the weight of the candidates whilemaintaining the downward closure property of these patterns which allowsto accelerate the process of candidate generation. |
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| ISSN: | 1312-6555 1314-7897 1314-7897 |
| DOI: | 10.55630/sjc.2015.9.105-122 |