Results 181 to 190 of about 45,837 (241)

Mining Frequent and Homogeneous Closed Itemsets

2016
It is well known that when mining frequent itemsets from a transaction database, the output is usually too large to be effectively exploited by users. To cope with this difficulty, several forms of condensed representations of the set of frequent itemsets have been proposed, among which the notion of closure is one of the most popular.
Hilali, Ines, Marinica, Claudia, Jen, Tao-Yuan, Laurent, Dominique, Ben Yahia, Sadok   +4 more
openaire   +2 more sources

Distributed Frequent Closed Itemsets Mining

2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System, 2007
As many large organizations have multiple data sources and the scale of dataset becomes larger and larger, it is inevitable to carry out data mining in the distributed environment. In this paper, we address the problem of mining global frequent closed itemsets in distributed environment.
Chun Liu, Zheng Zheng, Kai-Yuan Cai, Shichao Zhang   +3 more
openaire   +1 more source

Accelerating probabilistic frequent itemset mining

Proceedings of the 19th ACM international conference on Information and knowledge management, 2010
Data uncertainty is inherent in emerging applications such as location-based services, sensor monitoring systems, and data integration. To handle a large amount of imprecise information, uncertain databases have been recently developed. In this paper, we study how to efficiently discover frequent itemsets from large uncertain databases, interpreted ...
Lee, SD, Wang, L, Cheng, R, Cheung, DW
openaire   +2 more sources

Mining Frequent Closed Itemsets from Distributed Repositories

2007
In this paper we address the problem of mining frequent closed itemsets in a highly distributed setting like a Grid. The extraction of frequent (closed) itemsets is an important problem in Data Mining, and is a very expensive phase needed to extract from a transactional database a reduced set of meaningful association rules, typically used for Market ...
LUCCHESE, Claudio, ORLANDO, Salvatore, R. PEREGO, SILVESTRI, Claudio   +3 more
openaire   +3 more sources

Memory Efficient Frequent Itemset Mining

2018
Frequent itemset mining has been one of the most popular data mining techniques. Despite a large number of algorithms developed to implement this functionality, there is still room for improvement of their efficiency. In this paper, we focus on memory use in frequent itemset mining.
Nima Shahbazi, Rohollah Soltani, Jarek Gryz   +2 more
openaire   +1 more source

Parametric Algorithms for Mining Share Frequent Itemsets

Journal of Intelligent Information Systems, 2000
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Barber, Brock, Hamilton, Howard J.
openaire   +2 more sources

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
openaire   +1 more source

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
openaire   +1 more source

Mining Frequent Itemsets Using Improved Apriori on Spark

International Conference on Information System and Data Mining, 2019
Finding the frequent itemset is one of the most investigated extents of data mining. The Apriori algorithm is the most established algorithm for frequent itemset mining, but it has issues regarding scanning frequent databases and generating a large ...
Fei Gao, Ashutosh Khandelwal, Jiangjiang Liu   +2 more
semanticscholar   +1 more source

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
openaire   +1 more source