MINING RARE RULES USING ASSOCIATIVE CLASSIFICATION.
The fusion of Association Rule Mining (ARM) and classification results in Associative Classification (AC) that attains higher accuracy than traditional classification algorithms. But it ignores the rare itemsets during mining process. Frequently occurring items will be associated with one another in...
| Publicado en: | Turkish Journal of Physiotherapy Rehabilitation Vol. 32; no. 2; pp. 1998 - 2006 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Turkish Journal of Physiotherapy & Rehabilitation
2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | The fusion of Association Rule Mining (ARM) and classification results in Associative Classification (AC) that attains higher accuracy than traditional classification algorithms. But it ignores the rare itemsets during mining process. Frequently occurring items will be associated with one another in enormous number ways and can be extracted simply because the items are so common. Extracting frequent rules using ARM is also an imperative field of research. But rare rules occur infrequently and it has a vital part in numerousdisciplines like scientific, evolutionary and monetaryareas. The rare pattern mining gets inclined towards discovery of certain unrevealed/unpredicted occurrences and it is more valuable to learn. Finding rare association rule is like finding a valuable treasure in a ground. This process is a very daunting task but it gives more rewards to the user once it is successful. The main goal of the proposed Rare Associative Classification (RAC) algorithm is to discover the rare rules among the set of itemsets in a database that occur infrequently and useful for further decision making. This chapter outlines various studies and present research on Rare Associative Classification. |
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