Results 191 to 200 of about 45,837 (241)

An efficient parallel row enumerated algorithm for mining frequent colossal closed itemsets from high dimensional datasets

Information Sciences, 2019
Mining colossal itemsets from high dimensional datasets have gained focus in recent times. The conventional algorithms expend most of the time in mining small and mid-sized itemsets, which do not enclose valuable and complete information for decision ...
Manjunath K. Vanahalli, Nagamma Patil
semanticscholar   +1 more source

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
openaire   +2 more sources

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
openaire   +2 more sources

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
openaire   +1 more source

Privacy-preserving federated mining of frequent itemsets

Information Sciences, 2023
Yao Chen, Wensheng Gan, Yongdong Wu, Philip S. Yu   +3 more
semanticscholar   +1 more source

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
openaire   +1 more source

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
openaire   +1 more source

ANG: a combination of Apriori and graph computing techniques for frequent itemsets mining

Journal of Supercomputing, 2019
Rui Zhang, Wenguang Chen, Tse-Chuan Hsu, Hongji Yang, Yeh-Ching Chung   +4 more
semanticscholar   +1 more source

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
openaire   +1 more source

SS-FIM: Single Scan for Frequent Itemsets Mining in Transactional Databases

Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2017
Y. Djenouri, M. Comuzzi, D. Djenouri
semanticscholar   +1 more source