Results 1 - 10 of 154

CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets

by Jian Pei, Jiawei Han, Runying Mao , 2000
"... Association mining may often derive an undesirably large set of frequent itemsets and association rules. Recent studies have proposed an interesting alternative: mining frequent closed itemsets and their corresponding rules, which has the same power as association mining but substantially reduces th ..."
Abstract - Cited by 312 (28 self) - Add to MetaCart
single prefix path compression technique to identify frequent closed itemsets quickly, and (3) exploring a partition-based projection mechanism for scalable mining in large databases. Our performance study shows that CLOSET is efficient and scalable over large databases, and is faster than the previously

Frequent Patterns mining in time-sensitive Data Stream

by Mohamed Salah Gouider, Manel Zarrouk - International Journal of Computer Science Issues , 2012
"... Mining frequent itemsets through static Databases has been extensively studied and used and is always considered a highly challenging task. For this reason it is interesting to extend it to data streams field. In the streaming case, the frequent patterns’ mining has much more information to track an ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
of mining time-sensitive data streams. We tried to improve an existing algorithm by increasing the temporal accuracy and discarding the out-of-date data by adding a new concept called the “Shaking Point”. We presented as well some experiments illustrating the time and space required.

AN EFFICIENT ALGORITHM FOR MINING FREQUENT ITEMSETS OVER DATA STREAMS UNDER THE TIME- SENSITIVE SLIDING-WINDOW MODEL

by V. Soujanya, S. Ramanaiah
"... Mining frequent itemsets has been widely studied over the last decade, mostly focuses on mining frequent itemsets from static databases. In many of the new applications, data flow through the internet or sensor networks which extend the mining techniques to a dynamic environment. The main challenges ..."
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challenges include a quick response to the continuous request, a compact summary of the data stream, and a mechanism that adapts to the limited resources. Here, we propose a time-sensitive sliding window model for mining frequent itemsets from data streams. Our approach consists of a storage structure

Mining frequent itemsets from data streams with a time-sensitive sliding window

by Chih-hsiang Lin, Ding-ying Chiu, Yi-hung Wu - In SDM , 2005
"... Mining frequent itemsets has been widely studied over the last decade. Past research focuses on mining frequent itemsets from static databases. In many of the new applications, data flow through the Internet or sensor networks. It is challenging to extend the mining techniques to such a dynamic envi ..."
Abstract - Cited by 23 (0 self) - Add to MetaCart
environment. The main challenges include a quick response to the continuous request, a compact summary of the data stream, and a mechanism that adapts to the limited resources. In this paper, we develop a novel approach for mining frequent itemsets from data streams based on a time-sensitive sliding window

An Efficient Approach for Mining Frequent Itemsets with Large Windows

by K Jothimani, K Jothimani, S. Antony, Selvadoss Thanmani
"... Abstract — The problem of mining frequent itemsets in streaming data has attracted a lot of attention lately. Even though numerous frequent itemsets mining algorithms have been developed over the past decade, new solutions for handling stream data are still required due to the continuous, unbounded, ..."
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the input data several times. The goal of this article analysing the mining frequent itemsets in theoretical manner in the large windows. By comparing previous algorithms we propose new method using analytical modelling to determine the factors over data streams.

An Efficient Algorithm for Mining Frequent Itemsets over the entire History of Data Streams

by Hua-fu Li, Suh-yin Lee, Man-kwan Shan - In Proc. of First International Workshop on Knowledge Discovery in Data Streams , 2004
"... Abstract. A data stream is a continuous, huge, fast changing, rapid, infinite sequence of data elements. The nature of streaming data makes it essential to use online algorithms which require only one scan over the data for knowledge discovery. In this paper, we propose a new single-pass algorithm, ..."
Abstract - Cited by 49 (3 self) - Add to MetaCart
, called DSM-FI (Data Stream Mining for Frequent Itemsets), to mine all frequent itemsets over the entire history of data streams. DSM-FI has three major features, namely single streaming data scan for counting itemsets ’ frequency information, extended prefix-tree-based compact pattern representation

TIFIM: Tree based Incremental Frequent Itemset Mining over Streaming Data

by V. Sidda Reddy, Dr T. V. Rao, Dr A. Govardhan
"... Data Stream Mining algorithms performs under constraints called space used and time taken, which is due to the streaming property. The relaxation in these constraints is inversely proportional to the streaming speed of the data. Since the caching and mining the streaming-data is sensitive, here in t ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
in this paper a scalable, memory efficient caching and frequent itemset mining model is devised. The proposed model is an incremental approach that builds single level multi node trees called bushes from each window of the streaming data; henceforth we refer this proposed algorithm as a Tree (bush) based

Mining Frequent Itemsets Over Tuple-evolving Data Streams

by Chongsheng Zhang, Mirjana Mazuran, Hamid Mousavi, Yuan Hao, Carlo Zaniolo, Florent Masseglia
"... In many data streaming applications today, tuples inside the streams may get revised over time. This type of data stream brings new issues and challenges to the data mining tasks. We present a theoretical analysis for mining frequent item-sets from sliding windows over such data. We define condi-tio ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
In many data streaming applications today, tuples inside the streams may get revised over time. This type of data stream brings new issues and challenges to the data mining tasks. We present a theoretical analysis for mining frequent item-sets from sliding windows over such data. We define condi

Mining Frequent Itemsets with Normalized Weight

by Younghee Kim, Wonyoung Kim, Ungmo Kim - in Continuous Data Streams, JIPS
"... Abstract—A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. The continuous characteristic of streaming data necessitates the use of algorithms that require only one scan over the stream for knowledge discovery. Data mining over data streams should ..."
Abstract - Cited by 4 (0 self) - Add to MetaCart
support the flexible trade-off between processing time and mining accuracy. In many application areas, mining frequent itemsets has been suggested to find important frequent itemsets by considering the weight of itemsets. In this paper, we present an efficient algorithm WSFI (Weighted Support Frequent

doi:10.1093/comjnl/bxs010 An Efficient Frequent Itemset Mining Method over High-speed Data Streams

by Mina Memar, Mahmood Deypir, Mohammad Hadi Sadreddini, Seyyed Mostafa Fakhrahmad , 2011
"... Frequent itemset mining over sliding window is an interesting problem and has a large number of applications. Sliding window is a widely used model for frequent itemset mining over data streams due to its capability to handle concept drift, its bounded memory and its low processing time.A sliding wi ..."
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Frequent itemset mining over sliding window is an interesting problem and has a large number of applications. Sliding window is a widely used model for frequent itemset mining over data streams due to its capability to handle concept drift, its bounded memory and its low processing time.A sliding
Results 1 - 10 of 154