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IIS-Mine: A new efficient method for mining frequent itemsets |
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| รหัสดีโอไอ | |
| Creator | 1. Supatra Sahaphong 2. Veera Boonjing |
| Title | IIS-Mine: A new efficient method for mining frequent itemsets |
| Publisher | Maejo University |
| Publication Year | 2555 |
| Journal Title | Maejo International Journal of Science and Technology |
| Journal Vol. | 6 |
| Journal No. | 1 |
| Page no. | 130 |
| Keyword | association rule mining,data mining,frequent itemsets mining,frequent patterns mining,knowledge discovering |
| ISSN | 1905-7873 |
| Abstract | A new approach to mine all frequent itemsets from a transaction database is proposed. The main features of this paper are as follows: (1) the proposed algorithm performs database scanning only once to construct a data structure called an inverted index structure (IIS); (2) the change in the minimum support threshold is not affected by this structure, and as a result, a rescan of the database is not required; and (3) the proposed mining algorithm, IIS-Mine, uses an efficient property of an extendable itemset, which reduces the recursiveness of mining steps without generating candidate itemsets, allowing frequent itemsets to be found quickly. We have provided definitions, examples, and a theorem, the completeness and correctness of which is shown by mathematical proof. We present experiments in which the run time, memory consumption and scalability are tested in comparison with a frequent-pattern (FP) growth algorithm when the minimum support threshold is varied. Both algorithms are evaluated by applying them to synthetics and real-world datasets. The experimental results demonstrate that IIS-Mine provides better performance than FP-growth in terms of run time and space consumption and is effective when used on dense datasets. |
