Date of Publication :27th April 2018
Abstract: Data Mining is the process of extracting useful information or patterns from the data in large relational databases. In data mining, frequent pattern mining plays an important role for finding correlations among data. Similarly infrequent patterns are finding the uninteresting patterns that are rarely found in the database provide useful information. The aim of Infrequent Pattern Mining is to mine patterns which are not frequent that is the support value less than the threshold. Infrequent pattern mining (IPM) also plays a major role in the field of research and it has wide application domains such as medical, banking, biology, market basket analysis, telecommunication etc. Mining infrequent patterns from large dataset is challenging. In this paper we focus on study of various existing infrequent pattern mining techniques like Positive and Negative Association Rule (PNAR), Minimal Infrequent Itemset Mining (MIIM), Rare Association Rule (RAR), Pattern-Growth Paradigm and Residual Trees, Optimization Rule Based and Confabulation Inspired Association Rule Mining
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