Results 151 to 160 of about 8,356 (188)

Mining Frequent Weighted Closed Itemsets

2013
Mining frequent itemsets plays an important role in mining association rules. One of methods for mining frequent itemsets is mining frequent weighted itemsets (FWIs). However, the number of FWIs is often very large when the database is large. Besides, FWIs will generate a lot of rules and some of them are redundant.
Bay Vo, Nhu-Y Tran, Duong-Ha Ngo
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Mining Maximal Frequent Itemsets with Frequent Pattern List

Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), 2007
Mining frequent itemsets is a major aspect of association rule research. However, the mining of the complete of frequent itemsets will lead to a huge number of itemsets. Fortunately, this problem can be reduced to the mining of maximal frequent itemsets.
Jin Qian, Feiyue Ye
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Mining frequent itemset from uncertain data

2011 International Conference on Electrical and Control Engineering, 2011
We study the problem of mining frequent itemset from probabilistic data. Firstly, to solve the semantic corruption brought by expected frequent itemset conception, we define the probabilistic frequent itemset which is consistent with possible world model and holds the apriori property.
Feng Gao, Chengrong Wu
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Mining Frequent Gradual Itemsets from Large Databases

2009
Mining gradual rules plays a crucial role in many real world applications where huge volumes of complex numerical data must be handled, e.g., biological databases, survey databases, data streams or sensor readings. Gradual rules highlight complex order correlations of the form. The more/less X, then the more/less Y .
Di Jorio, Lisa, Laurent, Anne, Teisseire, Maguelonne   +2 more
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Fast mining frequent itemsets using Nodesets

Expert Systems with Applications, 2014
Node-list and N-list, two novel data structure proposed in recent years, have been proven to be very efficient for mining frequent itemsets. The main problem of these structures is that they both need to encode each node of a PPC-tree with pre-order and post-order code.
Zhi-Hong Deng, Sheng-Long Lv
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Verified Programs for Frequent Itemset Mining

2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2018
Frequent itemset mining is one pillar of machine learning and is very important for many data mining applications. There are many different algorithms for frequent itemset mining, but to our knowledge no implementation has been proven correct using computer aided verification. Hu et al. derived on paper an efficient algorithm for this problem, starting
Loulergue, Frédéric, Whitney, Christopher   +1 more
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Frequent itemset mining on graphics processors

Proceedings of the Fifth International Workshop on Data Management on New Hardware, 2009
We present two efficient Apriori implementations of Frequent Itemset Mining (FIM) that utilize new-generation graphics processing units (GPUs). Our implementations take advantage of the GPU's massively multi-threaded SIMD (Single Instruction, Multiple Data) architecture.
Wenbin Fang, Mian Lu, Xiangye Xiao, Bingsheng He, Qiong Luo   +4 more
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Mining Frequent Itemsets with Dualistic Constraints

2012
Mining frequent itemsets can often generate a large number of frequent itemsets. Recent studies proposed mining itemset with the different types of constraint. The paper is to mine frequent itemsets, where a one: does not contain any item of C0 or contains at least one item of C0.
Anh Tran, Hai Duong, Tin Truong, Bac Le
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Mining Frequent Itemsets from Multidimensional Databases

2011
Mining frequent itemsets (FIs) has been developing in recent years. However, little attention has been paid to efficient methods for mining in multidimensional databases. In this paper, we propose a new method with a supporting structure called AIO-tree (Attributes Itemset Object identifications - tree) for mining FIs from multidimensional databases ...
Bay Vo, Bac Le, Thang N. Nguyen
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Frequent Itemset Mining with Parallel RDBMS

2005
Data mining on large relational databases has gained popularity and its significance is well recognized. However, the performance of SQL based data mining is known to fall behind specialized implementation. We investigate approaches based on SQL for the problem of finding frequent patterns from a transaction table, including an algorithm that we ...
Xuequn Shang, Kai-Uwe Sattler
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