EAHUIM: Enhanced Absolute High Utility Itemset Miner for Big Data
High utility itemset mining (HUIM) is a data mining technique that identifies the itemsets with utility levels exceeding a pre-determined threshold. The factor utility is described as the combination of magnitude and element of significance for an item, and the algorithm objectives to locate the set...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-04-01
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| Series: | International Journal of Information Management Data Insights |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667096821000483 |
| _version_ | 1818161923055681536 |
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| author | Vandna Dahiya Sandeep Dalal |
| author_facet | Vandna Dahiya Sandeep Dalal |
| author_sort | Vandna Dahiya |
| collection | DOAJ |
| description | High utility itemset mining (HUIM) is a data mining technique that identifies the itemsets with utility levels exceeding a pre-determined threshold. The factor utility is described as the combination of magnitude and element of significance for an item, and the algorithm objectives to locate the set of items with a utility higher or equivalent to a set benchmark. These itemsets are utilized to build association rules for data mining systems. However, in the age of big data, conventional (HUIM) strategies are least effective with limited processing capabilities. This work proposes an optimized technique, Enhanced Absolute High Utility Itemset Miner (EAHUIM) by incorporating various refinements into the Absolute High Utility Itemset Miner (AHUIM) Algorithm. EAHUIM discovers the itemsets from large datasets in near real-time and serves as the foundation for information management and decision-making systems by providing diverse insights. The experimental analysis reveals that EAHUIM outclasses other state-of-the-art algorithms for HUIM. |
| first_indexed | 2024-12-11T16:25:29Z |
| format | Article |
| id | doaj.art-6b7e6f6a6c454a5fa1403e5366166dd3 |
| institution | Directory Open Access Journal |
| issn | 2667-0968 |
| language | English |
| last_indexed | 2024-12-11T16:25:29Z |
| publishDate | 2022-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Information Management Data Insights |
| spelling | doaj.art-6b7e6f6a6c454a5fa1403e5366166dd32022-12-22T00:58:44ZengElsevierInternational Journal of Information Management Data Insights2667-09682022-04-0121100055EAHUIM: Enhanced Absolute High Utility Itemset Miner for Big DataVandna Dahiya0Sandeep Dalal1Corresponding author: Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, India.; Department of Computer Science & Applications, Maharshi Dayanand University, Haryana, IndiaDepartment of Computer Science & Applications, Maharshi Dayanand University, Haryana, IndiaHigh utility itemset mining (HUIM) is a data mining technique that identifies the itemsets with utility levels exceeding a pre-determined threshold. The factor utility is described as the combination of magnitude and element of significance for an item, and the algorithm objectives to locate the set of items with a utility higher or equivalent to a set benchmark. These itemsets are utilized to build association rules for data mining systems. However, in the age of big data, conventional (HUIM) strategies are least effective with limited processing capabilities. This work proposes an optimized technique, Enhanced Absolute High Utility Itemset Miner (EAHUIM) by incorporating various refinements into the Absolute High Utility Itemset Miner (AHUIM) Algorithm. EAHUIM discovers the itemsets from large datasets in near real-time and serves as the foundation for information management and decision-making systems by providing diverse insights. The experimental analysis reveals that EAHUIM outclasses other state-of-the-art algorithms for HUIM.http://www.sciencedirect.com/science/article/pii/S2667096821000483Big dataItemset miningParallel computingRecommendation systemSpark |
| spellingShingle | Vandna Dahiya Sandeep Dalal EAHUIM: Enhanced Absolute High Utility Itemset Miner for Big Data International Journal of Information Management Data Insights Big data Itemset mining Parallel computing Recommendation system Spark |
| title | EAHUIM: Enhanced Absolute High Utility Itemset Miner for Big Data |
| title_full | EAHUIM: Enhanced Absolute High Utility Itemset Miner for Big Data |
| title_fullStr | EAHUIM: Enhanced Absolute High Utility Itemset Miner for Big Data |
| title_full_unstemmed | EAHUIM: Enhanced Absolute High Utility Itemset Miner for Big Data |
| title_short | EAHUIM: Enhanced Absolute High Utility Itemset Miner for Big Data |
| title_sort | eahuim enhanced absolute high utility itemset miner for big data |
| topic | Big data Itemset mining Parallel computing Recommendation system Spark |
| url | http://www.sciencedirect.com/science/article/pii/S2667096821000483 |
| work_keys_str_mv | AT vandnadahiya eahuimenhancedabsolutehighutilityitemsetminerforbigdata AT sandeepdalal eahuimenhancedabsolutehighutilityitemsetminerforbigdata |