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Mining erasable itemsets with subset and superset itemset constraints
Expert Systems With Applications, 2017Abstract Erasable itemset (EI) mining, a branch of pattern mining, helps managers to establish new plans for the development of new products. Although the problem of mining EIs was first proposed in 2009, many efficient algorithms for mining these have since been developed. However, these algorithms usually require a lot of time and memory usage.
Bay Vo, Sung Wook Baik, Tuong Le
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International Journal of Information Technology & Decision Making, 2010
High utility itemsets mining identifies itemsets whose utility satisfies a given threshold. It allows users to quantify the usefulness or preferences of items using different values. Thus, it reflects the impact of different items. High utility itemsets mining is useful in decision-making process of many applications, such as retail marketing and Web ...
Ying Liu 0039 +4 more
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High utility itemsets mining identifies itemsets whose utility satisfies a given threshold. It allows users to quantify the usefulness or preferences of items using different values. Thus, it reflects the impact of different items. High utility itemsets mining is useful in decision-making process of many applications, such as retail marketing and Web ...
Ying Liu 0039 +4 more
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Mining Cohesive Itemsets in Graphs
2014Discovering patterns in graphs is a well-studied field of data mining. While a lot of work has already gone into finding structural patterns in graph datasets, we focus on relaxing the structural requirements in order to find items that often occur near each other in the input graph.
Tayena Hendrickx +2 more
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Geometrically Inspired Itemset Mining
Sixth International Conference on Data Mining (ICDM'06), 2006In our geometric view, an itemset is a vector (itemvector) in the space of transactions. Linear and potentially non-linear transformations can be applied to the itemvectors before mining patterns. Aggregation functions and interestingness measures can be applied to the transformed vectors and pushed inside the mining process.
Florian Verhein, Sanjay Chawla
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Efficient Skyline Itemsets Mining
Proceedings of the Eighth International C* Conference on Computer Science & Software Engineering - C3S2E '15, 2008Utility Mining (UM) in context of Market Basket Analysis consists of mining itemsets from a transaction database guided by optimizing utility. For example, UM consists of extracting all itemsets in a transaction database having utility above a user-defined minimum threshold or mining Top-K high utility itemset.
Vikram Goyal +2 more
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2017 IEEE International Conference on Big Data (Big Data), 2017
Erasable-itemset mining used in production planning identifies itemsets (or components) that, if removed, would not affect profits. Formally, an itemset is erasable if its gain ratio is equal to or smaller than a given maximum gain-ratio threshold r. Since new products with different components may be added, the original batch algorithm will waste time
Tzung-Pei Hong +4 more
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Erasable-itemset mining used in production planning identifies itemsets (or components) that, if removed, would not affect profits. Formally, an itemset is erasable if its gain ratio is equal to or smaller than a given maximum gain-ratio threshold r. Since new products with different components may be added, the original batch algorithm will waste time
Tzung-Pei Hong +4 more
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Mining high occupancy itemsets
Future Generation Computer Systems, 2020Abstract Frequent itemset mining has been extensively studied in data mining for over the last two decades because of its numerous applications. However, the classic support-based mining framework used by most previous studies is not suitable for some real-world applications, such as the travel landscapes recommendation, where o c c u p a n
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Third IEEE International Conference on Data Mining, 2004
Traditional association rule mining algorithms only generate a large number of highly frequent rules, but these rules do not provide useful answers for what the high utility rules are. We develop a novel idea of top-K objective-directed data mining, which focuses on mining the top-K high utility closed patterns that directly support a given business ...
Raymond Chan +2 more
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Traditional association rule mining algorithms only generate a large number of highly frequent rules, but these rules do not provide useful answers for what the high utility rules are. We develop a novel idea of top-K objective-directed data mining, which focuses on mining the top-K high utility closed patterns that directly support a given business ...
Raymond Chan +2 more
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Frequent Itemset Mining for Big Data
2013 IEEE International Conference on Big Data, 2013Frequent Itemset Mining (FIM) is one of the most well known techniques to extract knowledge from data. The combinatorial explosion of FIM methods become even more problematic when they are applied to Big Data. Fortunately, recent improvements in the field of parallel programming already provide good tools to tackle this problem.
Sandy Moens +2 more
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Knowledge Compilation for Itemset Mining
2010We present a novel approach to itemset mining whereby the set of all itemsets are compiled into a compact form, closely related to binary decision diagrams. While there were previous attempts to utilize decision diagrams for storing the set of frequent itemsets this is the first approach that does not rely on backtrack search to generate such a set ...
Hadrien Cambazard +2 more
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