Results 131 to 140 of about 1,634,075 (173)

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.
Inès Hilali, Tao-Yuan Jen, Dominique Laurent 0001, Claudia Marinica, Sadok Ben Yahia   +4 more
openaire   +2 more sources

CloseMiner: Discovering Frequent Closed Itemsets Using Frequent Closed Tidsets

Fifth IEEE International Conference on Data Mining (ICDM'05), 2006
Complete set of itemsets can be grouped into non-overlapping clusters identified by closed tidsets. Each cluster has only one closed itemset and is the superset of all itemsets with the same support. Number of closed itemsets is identical to the number of clusters.
Ningthoujam Gourakishwar Singh, Sanasam Ranbir Singh, Anjana Kakoti Mahanta   +2 more
openaire   +2 more sources

NEclatClosed: A vertical algorithm for mining frequent closed itemsets

Expert Systems with Applications, 2021
Abstract Frequent closed itemsets provide a lossless and concise collection of all frequent itemsets to reduce the runtime and memory requirement of frequent itemsets mining tasks. This study presents an algorithm named NEclatClosed for fast mining of frequent closed itemsets.
Nader Aryabarzan, Behrouz Minaei-Bidgoli
openaire   +1 more source

Mining frequent closed itemsets for large data

2004 International Conference on Machine Learning and Applications, 2004. Proceedings., 2005
Mining frequent closed itemsets is one effective method to analyse frequent pattern, and further, to generate association rules. Several algorithms were proposed to generate frequent closed itemsets, including CLOSE, A-CLOSE, CLOSET, CHARM and CLOSET + etc. However it's still hard for these algorithms to deal with dense and very large data.
Huaiguo Fu, Engelbert Mephu Nguifo
openaire   +1 more source

Incrementally building frequent closed itemset lattice

Expert Systems with Applications, 2014
A concept lattice is an ordered structure between concepts. It is particularly effective in mining association rules. However, a concept lattice is not efficient for large databases because the lattice size increases with the number of transactions. Finding an efficient strategy for dynamically updating the lattice is an important issue for real-world ...
Phuong-Thanh La, Bac Le, Bay Vo
openaire   +1 more source

Fast approximation of probabilistic frequent closed itemsets

Proceedings of the 50th Annual Southeast Regional Conference, 2012
In recent years, the concept of and algorithm for mining probabilistic frequent itemsets (PFIs) in uncertain databases, based on possible worlds semantics and a dynamic programming approach for frequency calculations, has been proposed. The frequentness of a given itemset in this scheme can be characterized by the Poisson binomial distribution. Further
Erich A. Peterson, Peiyi Tang
openaire   +1 more source

A-Close+: An Algorithm for Mining Frequent Closed Itemsets

2008 International Conference on Advanced Computer Theory and Engineering, 2008
Association Rule Mining (ARM) is the most essential technique for data mining that mines hidden associations between data in large databases. The most important function of ARM is to find frequent itemsets. Frequent closed itemsets (FCI) is an important condense representation method for frequent itemsets, and because of its importance in recent years,
Maryam Shekofteh, Amir Masoud Rahmani, Mashaallah Abbasi Dezfuli   +2 more
openaire   +1 more source

An Optimization to CHARM Algorithm for Mining Frequent Closed Itemsets

2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, 2015
Frequent itemsets which are quite useful in many applications always suffer from their huge number and information redundancy. Frequent closed itemsets that provide a minimal and lossless presentation of all frequent itemsets are a solution to the problem.
Xin Ye, Feng Wei 0001, Fan Jiang 0005, Shaoyin Cheng   +3 more
openaire   +1 more source

An Improved MapReduce Algorithm for Mining Closed Frequent Itemsets

2016 IEEE International Conference on Software Science, Technology and Engineering (SWSTE), 2016
Mining closed frequent item sets is a key objective in the field of data mining due to its wide range of applications. Given a database of transactions, the task is to find closed subsets which appear frequently in different transactions. This subject has been studied thoroughly, and many efficient algorithms had been presented, however, most of them ...
Yaron Gonen, Ehud Gudes
openaire   +2 more sources

MapReduce-Based Balanced Mining for Closed Frequent Itemset

2012 IEEE 19th International Conference on Web Services, 2012
Mining closed frequent itemset (CFI) plays an essential role in many real-world data mining applications. With the emergence of abundant large-scale data sets, it now turns to be a significant and challenging issue to mine CFI concurrently. This paper proposes a parallel balanced mining algorithm for CFI based on the MapReduce platform.
Guang-Peng Chen, Yu-Bin Yang, Yao Zhang
openaire   +2 more sources