Mining constrained frequent itemsets from distributed uncertain data |
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| Affiliation: | 1. Centre for Operational Research and Logistics, Department of Mathematics, University of Portsmouth, Lion Gate Building, Lion Terrace, Portsmouth PO1 3HF, UK;2. Department of Industrial Engineering, Institut Teknologi Nasional, Bandung 40124, Indonesia;3. Centre for Logistics & Heuristic Optimization (CLHO), Kent Business School, University of Kent, Canterbury, Kent CT2 7PE, UK;4. Othman Yeop Abdullah Graduate School of Business, Universiti Utara Malaysia, 06010 UUM Sintok, Kedah Darul Aman, Malaysian;1. Department of Industrial & Operations Engineering, University of Michigan, Ann Arbor, USA;2. Theoretical Division (T-5), Los Alamos National Laboratory, NM, USA |
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| Abstract: | Nowadays, high volumes of massive data can be generated from various sources (e.g., sensor data from environmental surveillance). Many existing distributed frequent itemset mining algorithms do not allow users to express the itemsets to be mined according to their intention via the use of constraints. Consequently, these unconstrained mining algorithms can yield numerous itemsets that are not interesting to users. Moreover, due to inherited measurement inaccuracies and/or network latencies, the data are often riddled with uncertainty. These call for both constrained mining and uncertain data mining. In this journal article, we propose a data-intensive computer system for tree-based mining of frequent itemsets that satisfy user-defined constraints from a distributed environment such as a wireless sensor network of uncertain data. |
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| Keywords: | Data mining Frequent pattern mining Advanced data-intensive computing algorithms Constraints Distributed computing |
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