Mining frequent patterns from large databases plays a vital role in many data mining tasks and has a broad range of applications. Most previously proposed algorithms have been specifically designed for one type of dataset thus making them unsuitable for a range of datasets. There have been a few techniques suggested to provide performance for these association rules mining algorithms. However, these algorithms do not support a high level of user interaction, relying only on the classic support and confidence metrics for expressing user requirements. On the other hand, techniques exist that focus on improving the level of user interaction at the cost of performance.In this work, we propose a new algorithm, FOLD-growth with Constraints (FGC), which not only provides user interaction but also improves performance over existing popular algorithms. It embeds the user defined constraints into a pre-processing structure to generate constraint satisfied itemsets and uses this result to build a highly compact data structure. Interestingly, the constraint embedding technique makes existing pattern growth methods not only efficient but also highly effective over a range of datasets, irrespective of their data distribution. The technique also supports the use of conjunctions of different types of commonly used constraints.
| Identifer | oai:union.ndltd.org:ADTP/173230 |
| Creators | Kutty, Sangeetha |
| Publisher | AUT University |
| Source Sets | Australiasian Digital Theses Program |
| Detected Language | English |
| Rights | All items in ScholarlyCommons@AUT are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated. |
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