Abstract
Association Rule Mining (ARM) is a well recognized and interesting area of research in the field of Data Mining. In ARM, significant amount of research work has been reported. On the contrary, very less work has been reported for Negative Association Rule Mining (NARM). ARM concentrates only on positive rules while NARM explores negative rules. Also, most researchers have worked with discrete transaction dataset. So, in this paper, we propose a technique called Negative and Positive Fuzzy Association Rule Mining (NP-FARM), which mines both negative and positive association rules from a fuzzy transaction dataset. NP-FARM algorithm has been implemented and the experimental results determine the optimal minimum support threshold and optimal minimum confidence threshold for the given dataset. Also, the experimental results demonstrate that, as the size of the dataset increases, a negligible change in execution time is witnessed to mine the growing dataset. Keywords: Association Rule Mining. Fuzzy Transactions, Positive and Negative Item Sets
Highlights
Data mining is defined as the process of extracting useful information from raw data by applying some of the wide range of data mining techniques which includes classification, clustering, etc..,1–2 Frequent item set mining is one of the most popular technique used to extract useful patterns from the given data which could be transactional, relational, spatial, temporal, spatio-temporal, time-series, etc.., Frequent item sets are those which appear most frequently in the data using which association rules can be derived
Most of the above methods used discrete transaction dataset while the Negative and Positive Fuzzy Association Rule Mining (NP-FARM) uses a Fuzzy transaction dataset and mines both negative and positive association rules
Fuzzy transaction data set containing 1000 transactions was given as input to NP-FARM
Summary
Data mining is defined as the process of extracting useful information from raw data by applying some of the wide range of data mining techniques which includes classification, clustering, etc.., Frequent item set mining is one of the most popular technique used to extract useful patterns from the given data which could be transactional, relational, spatial, temporal, spatio-temporal, time-series, etc.., Frequent item sets are those which appear most frequently in the data using which association rules can be derived. Frequent item sets could be considered as item sets whose support is greater than the minimum support threshold. Support indicates the frequency of occurrence of an item set in the given data. The association rule derived from the frequent item sets are considered to be strong if the confidence of the rule is greater than the minimum confidence threshold. In general, explores only positive rules of the form A=> B, where A ∩ B={} and. Several algorithms exist for mining this type of rules. In this paper, an algorithm (NP-FARM) is proposed, which explores both negative and positive rules from a Fuzzy transaction dataset
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