Abstract:
Fuzzy association rules use fuzzy logic to convert numerical attributes to fuzzy attributes, like ldquoIncome = Highrdquo, thus maintaining the integrity of information c...Show MoreMetadata
Abstract:
Fuzzy association rules use fuzzy logic to convert numerical attributes to fuzzy attributes, like ldquoIncome = Highrdquo, thus maintaining the integrity of information conveyed by such numerical attributes. On the other hand, crisp association rules use sharp partitioning to transform numerical attributes to binary ones like ldquoIncome = [100 K and above]rdquo, and can potentially introduce loss of information due to these sharp ranges. Fuzzy Apriori and its different variations are the only popular fuzzy association rule mining (ARM) algorithms available today. Like the crisp version of Apriori, fuzzy Apriori is a very slow and inefficient algorithm for very large datasets (in the order of millions of transactions). Hence, we have come up with a new fuzzy ARM algorithm meant for fast and efficient performance on very large datasets. As compared to fuzzy Apriori, our algorithm is 8-19 times faster for the very large standard real-life dataset we have used for testing with various mining workloads, both typical and extreme ones. A novel combination of features like two-phased multiple-partition tidlist-style processing, byte-vector representation of tidlists, and fast compression of tidlists contribute a lot to the efficiency in performance. In addition, unlike most two-phased ARM algorithms, the second phase is totally different from the first one in the method of processing (individual itemset processing as opposed to simultaneous itemset processing at each k-level), and is also many times faster. Our algorithm also includes an effective preprocessing technique for converting a crisp dataset to a fuzzy dataset.
Published in: 2009 IEEE International Conference on Fuzzy Systems
Date of Conference: 20-24 August 2009
Date Added to IEEE Xplore: 02 October 2009
ISBN Information:
Print ISSN: 1098-7584
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Large Datasets ,
- Association Rules ,
- Association Rule Mining ,
- Rule Mining Algorithm ,
- Association Rule Mining Algorithm ,
- Fuzzy Association Rule ,
- Fuzzy Association Rule Mining ,
- Fuzzy Logic ,
- Numerous Properties ,
- Fuzzy Rules ,
- Pre-processing Techniques ,
- Fuzzy Algorithm ,
- Real-life Datasets ,
- Binary Ones ,
- Source Code ,
- Support Values ,
- Membership Function ,
- Fuzzy Set ,
- Negative Ones ,
- Fuzzy Method ,
- Fuzzy Measure ,
- Fuzzy C-means Clustering ,
- Frequent Itemsets ,
- Fuzzy C-means ,
- Fuzzy Membership ,
- Huge Datasets ,
- Breadth-first Search ,
- Compression Algorithm ,
- Dataset Partition ,
- Nature Of The Dataset
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Large Datasets ,
- Association Rules ,
- Association Rule Mining ,
- Rule Mining Algorithm ,
- Association Rule Mining Algorithm ,
- Fuzzy Association Rule ,
- Fuzzy Association Rule Mining ,
- Fuzzy Logic ,
- Numerous Properties ,
- Fuzzy Rules ,
- Pre-processing Techniques ,
- Fuzzy Algorithm ,
- Real-life Datasets ,
- Binary Ones ,
- Source Code ,
- Support Values ,
- Membership Function ,
- Fuzzy Set ,
- Negative Ones ,
- Fuzzy Method ,
- Fuzzy Measure ,
- Fuzzy C-means Clustering ,
- Frequent Itemsets ,
- Fuzzy C-means ,
- Fuzzy Membership ,
- Huge Datasets ,
- Breadth-first Search ,
- Compression Algorithm ,
- Dataset Partition ,
- Nature Of The Dataset
- Author Keywords