Results 161 to 170 of about 739 (208)

Spatial Contextualization for Closed Itemset Mining

2018 IEEE International Conference on Data Mining (ICDM), 2018
We present the Spatial Contextualization for Closed Itemset Mining (SCIM) algorithm, an approach that builds a space for the target database in such a way that relevant itemsets can be retrieved regarding the relative spatial location of their items.
Altobelli B. Mantuan, Leandro Augusto Frata Fernandes   +1 more
openaire   +1 more source

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

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

Finding Closed Itemsets in Data Streams

2005
Closed itemset mining is a difficult problem especially when we consider the task in the context of a data stream. Compared to mining from a static transaction data set, the streaming case has far more information to track and far greater complexity to manage.
Hai Wang, Wenyuan Li 0005, Zeng-zhi Li, Lin Fan   +3 more
openaire   +1 more source

Efficient closed high-utility itemset mining

Proceedings of the 31st Annual ACM Symposium on Applied Computing, 2016
This paper presents a novel algorithm for discovering closed high-utility itemsets (CHUIs) efficiently. It proposes three strategies to mine CHUIs efficiently: closure jumping, forward closure checking and backward closure checking. It also relies on two new upper-bounds named local utility and sub-tree utility to prune the search space, and a Fast ...
Philippe Fournier-Viger, Souleymane Zida, Jerry Chun-Wei Lin, Cheng-Wei Wu, Vincent S. Tseng   +4 more
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

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

An Algorithm for Mining Lower Closed Itemsets

2004
The generalized association rule base(GARB) presented by Li(2003) can efficiently solve the problem of quantity of rule in the process of acquiring rule by traditional association rule mining algorithms. Therefore, how to deduce all rules contained in the rule of GARB becomes an urgent issue in order to support more effective decision-making.
Tianrui Li 0001, Ming Qing, Jun Ma 0002, Yang Xu 0001   +3 more
openaire   +1 more source

An Efficient Algorithm for Frequent Closed Itemsets Mining

2008 International Conference on Computer Science and Software Engineering, 2008
Efficient algorithms for mining frequent itemsets are crucial for mining association rules. Most existing work focuses on mining all frequent itemsets. However, since any subset of a frequent set also is frequent, it is sufficient to mine the set of frequent closed itemsets which determines exactly the complete set of all frequent itemsets and is ...
Lisheng Ma, Yi Qi
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