Results 31 to 40 of about 9,218 (205)
Mining Target-Oriented Sequential Patterns with Time-Intervals [PDF]
, 2010 A target-oriented sequential pattern is a sequential pattern with a concerned itemset in the end of pattern. A time-interval sequential pattern is a sequential pattern with time-intervals between every pair of successive itemsets.
Chueh, Hao-En
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Sliding Window-based Frequent Itemsets Mining over Data Streams using Tail Pointer Table [PDF]
International Journal of Computational Intelligence Systems, 2014 Mining frequent itemsets over transaction data streams is critical for many applications, such as wireless sensor networks, analysis of retail market data, and stock market predication.
Le Wang, Lin Feng, Bo Jin
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Binary image description using frequent itemsets
Journal of Big Data, 2020 In this paper, a novel method for binary image comparison is presented. We suppose that the image is a set of transactions and items. The proposed method applies along rows and columns of an image; this image is represented by all frequent itemset ...
Khalid Aznag +3 more
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Mining Top-K Frequent Itemsets Through Progressive Sampling
, 2010 We study the use of sampling for efficiently mining the top-K frequent itemsets of cardinality at most w. To this purpose, we define an approximation to the top-K frequent itemsets to be a family of itemsets which includes (resp., excludes) all very ...
Andrea Pietracaprina +8 more
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Axiomatization of frequent itemsets
Theoretical Computer Science, 2003 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Calders, Toon, Paredaens, J.
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Mining Frequent Itemsets in a Stream [PDF]
Seventh IEEE International Conference on Data Mining (ICDM 2007), 2007 We study the problem of finding frequent itemsets in a continuous stream of transactions. The current frequency of an itemset in a stream is defined as its maximal frequency over all possible windows in the stream from any point in the past until the current state that satisfy a minimal length constraint.
Calders, Toon +2 more
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An Efficient Rigorous Approach for Identifying Statistically Significant Frequent Itemsets [PDF]
, 2009 As advances in technology allow for the collection, storage, and analysis of vast amounts of data, the task of screening and assessing the significance of discovered patterns is becoming a major challenge in data mining applications.
Kirsch, Adam +5 more
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Mining Frequent Itemsets Using Genetic Algorithm
, 2010 In general frequent itemsets are generated from large data sets by applying association rule mining algorithms like Apriori, Partition, Pincer-Search, Incremental, Border algorithm etc., which take too much computer time to compute all the frequent ...
Biswas, Sushanta +3 more
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High Quality, Efficient Hierarchical Document Clustering Using Closed Interesting Itemsets [PDF]
, 2006 High dimensionality remains a significant challenge for document clustering. Recent approaches used frequent itemsets and closed frequent itemsets to reduce dimensionality, and to improve the efficiency of hierarchical document clustering. In this paper,
Kender, John R., Malik, Hassan H.
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Mining Productive Itemsets in Dynamic Databases
IEEE Access, 2020 Discovering frequent itemsets is a data analysis task used in numerous domains. It consists of finding sets of items (itemsets) that frequently appear in a set of database records (also called transactions). Though discovering frequent itemsets is useful,
Xiang Li +5 more
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