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DSM-FI: an efficient algorithm for mining frequent itemsets in data streams
2008
Knowledge and Information Systems
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In this paper, we propose a new single-pass algorithm, called DSM-FI (data stream mining for frequent itemsets), for online incremental mining of frequent itemsets over a continuous stream of online transactions ...
Online mining of data streams is an important data mining problem with broad applications. However, it is also a difficult problem since the streaming data possess some inherent characteristics. ...
Moreover, DSM-FI outperforms the well-known, single-pass algorithms BTS and StreamMining for mining frequent itemsets over the entire history of the streaming data. ...
doi:10.1007/s10115-007-0112-4
fatcat:v4ildu6wkbfp3eqdhw2h54ke74
Max-FISM: Mining (recently) maximal frequent itemsets over data streams using the sliding window model
2012
Computers and Mathematics with Applications
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In this paper, we propose an efficient algorithm, called Max-FISM (Maximal-Frequent Itemsets Mining), for mining recent maximal frequent itemsets from a high-speed stream of transactions within a sliding ...
Experimental studies show that the proposed Max-FISM algorithm is highly efficient in terms of memory and time complexity for mining recent maximal frequent itemsets over high-speed data streams. ...
The first effort to mine frequent itemsets over the entire history of data stream was proposed by Manku and Motwani [12] . ...
doi:10.1016/j.camwa.2012.01.045
fatcat:3lduoonbwjb6jda3yj62cys2o4
Efficient Maintenance and Mining of Frequent Itemsets over Online Data Streams with a Sliding Window
2006
2006 IEEE International Conference on Systems, Man and Cybernetics
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In this paper, we proposed an efficient one-pass algorithm, called MFI-TransSW (Mining Frequent Itemsets over a Transaction-sensitive Sliding Window), to mine the set of all frequent itemsets in data streams ...
Online mining of streaming data is one of the most important issues in data mining. ...
Li et al. proposed prefix tree-based single-pass algorithms, called DSM-FI [12] and DSM-MFI [13] , to mine the set of all frequent itemsets and maximal frequent itemsets over the entire history of offline ...
doi:10.1109/icsmc.2006.385267
dblp:conf/smc/LiHSL06
fatcat:d3wcu67yk5bfzcwxqtlwehnjei
Towards a new approach for mining frequent itemsets on data stream
2006
Journal of Intelligent Information Systems
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Mining frequent patterns on streaming data is a new challenging problem for the data mining community since data arrives sequentially in the form of continuous rapid streams. ...
At any time, users can issue requests for frequent itemsets over an arbitrary time interval. ...
Table The first approach for mining all frequent itemsets over the entire history of a streaming data was proposed by [10] where they define the first single-pass algorithm based on the anti-monotonic ...
doi:10.1007/s10844-006-0002-3
fatcat:eyhcuaebdfbfliptnfr44gis2y
An Efficient Approach To Mining Frequent Itemsets On Data Streams
2009
Zenodo
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The increasing importance of data stream arising in a wide range of advanced applications has led to the extensive study of mining frequent patterns. ...
Mining data streams poses many new challenges amongst which are the one-scan nature, the unbounded memory requirement and the high arrival rate of data streams. ...
Mining frequent sets over data streams presents interesting new challenges over traditional mining in static databases. ...
doi:10.5281/zenodo.1328918
fatcat:edxojrp4q5eghfroufeyiaxnia
Research issues in data stream association rule mining
2006
SIGMOD record
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There exist emerging applications of data streams that require association rule mining, such as network traffic monitoring and web click streams analysis. ...
This raises new issues that need to be considered when developing association rule mining techniques for stream data. ...
NNG05GA30G issued through the Office of Space Science and the OSU Grant. ...
doi:10.1145/1121995.1121998
fatcat:2l22o3uioja4nfy5iagmnqiduu
Mining frequent itemsets over data streams using efficient window sliding techniques
2009
Expert systems with applications
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Online mining of frequent itemsets over a stream sliding window is one of the most important problems in stream data mining with broad applications. ...
itemsets over data streams with a sliding window. ...
Acknowledgement The authors are grateful to the anonymous referees whose valuable comments helped to improve the content of this paper. ...
doi:10.1016/j.eswa.2007.11.061
fatcat:z5oduyhsfvfcljtw5mafnwkejy
SPEED : Mining Maxirnal Sequential Patterns over Data Strearns
2006
2006 3rd International IEEE Conference Intelligent Systems
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The main originality of our mining method is that we use a novel data structure to maintain frequent sequential patterns coupled with a fast pruning strategy. ...
In this paper we propose a new approach, called Speed (Sequential Patterns Efficient Extraction in Data streams), to identify frequent maximal sequential patterns in a data stream. ...
RELATED WORK In the recent years, data streams mining approaches mainly focused on maintaining frequent itemsets over the entire history of a streaming data. ...
doi:10.1109/is.2006.348478
fatcat:5wmsplbwmfai7hanp4jt7ktcma
A survey on algorithms for mining frequent itemsets over data streams
2007
Knowledge and Information Systems
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algorithms on mining frequent itemsets over data streams. ...
The increasing prominence of data streams arising in a wide range of advanced applications such as fraud detection and trend learning has led to the study of online mining of frequent itemsets (FIs). ...
Lossy Counting Algorithm Manku and Motwani [34] propose the Lossy Counting algorithm for computing an approximate set of FIs over the entire history of a stream. ...
doi:10.1007/s10115-007-0092-4
fatcat:vyauvbmmrvd7ni52ffpoqot76m
Incremental updates of closed frequent itemsets over continuous data streams
2009
Expert systems with applications
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Online mining of closed frequent itemsets over streaming data is one of the most important issues in mining data streams. ...
itemsets over recent data streams. ...
., 2005) , to mine the set of all frequent itemsets and maximal frequent itemsets over the entire history of offline data streams. ...
doi:10.1016/j.eswa.2007.12.054
fatcat:3udpbwkwm5gzxja6xqchtyieee
Mining Frequent Itemsets from Online Data Streams: Comparative Study
2013
International Journal of Advanced Computer Science and Applications
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The high complexity of the frequent itemsets mining problem hinders the application of the stream mining techniques. ...
In this review, we present a comparative study among almost all, as we are acquainted, the algorithms for mining frequent itemsets from online data streams. ...
It produces an approximate set of FIs over the entire history of a stream. The stream is divided into a sequence of buckets and each bucket consists of B = 1/ transactions. ...
doi:10.14569/ijacsa.2013.040717
fatcat:n2iqazipkrbvhni4uqht2u7cc4
Hyper-structure mining of frequent patterns in uncertain data streams
2012
Knowledge and Information Systems
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The second algorithm, TFUHS-Stream, is designed to find frequent itemsets in an uncertain data stream in a time-fading manner. ...
In this paper, we propose two hyper-structure-based false-positive-oriented algorithms to efficiently mine frequent itemsets from streams of uncertain data. ...
Over the past decade, a number of stream mining algorithms have been proposed to mine frequent patterns from data streams. ...
doi:10.1007/s10115-012-0581-y
pmid:24729652
pmcid:PMC3983695
fatcat:7el3kyleurdq7lv7sg2oov2pku
An Efficient Method for Mining Frequent Patterns based on Weighted Support over Data Streams
데이터 스트림에서 가중치 지지도 기반 빈발 패턴 추출 방법
2009
Journal of the Korea Academia-Industrial cooperation Society
데이터 스트림에서 가중치 지지도 기반 빈발 패턴 추출 방법
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In this paper, we propose an efficient method WSFI-Mine(Weighted Support Frequent Itemsets Mine) to mine all frequent itemsets by one scan from the data stream. ...
The continuous characteristic of streaming data necessitates the use of algorithms that require only one scan over the stream for knowledge discovery. ...
.: An Efficient Algorithm for Mining Frequent Itemsets over the Entire History of Data Streams. ...
doi:10.5762/kais.2009.10.8.1998
fatcat:o5opfiiy7baw7jq2dojsis5j7u
Dynamic Support Range based Rare Pattern Mining over Data Streams
2022
International Journal of Advanced Computer Science and Applications
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Rare itemset mining is a relatively recent topic of study in data mining. ...
The detected patterns are kept in a prefix-based rare pattern tree that uses double hashing to maintain the unusual pattern in the data stream. ...
The sequence is an interesting paradigm for tackling typical pattern mining issues since it doesn't need to examine the entire history of team and handling and could just handle a restricted range of recent ...
doi:10.14569/ijacsa.2022.0130378
fatcat:dmwwofdlbza7nbi7ujmgn2wqfu
Online Mining of Recent Music Query Streams
2006
2006 IEEE International Conference on Multimedia and Expo
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In this paper, we propose an online one-pass algorithm to mine the set of frequent temporal patterns in online music query streams with a sliding window. ...
Mining multimedia data is one of the most important issues in data mining. ...
[7] [8] proposed the novel online algorithms to find the complete set of maximal frequent melody structures and closed frequent melody structures over the entire history of a continuous music query stream ...
doi:10.1109/icme.2006.262948
dblp:conf/icmcs/LiHSL06
fatcat:gtryhm3qqrcavmvkpzl7djll24
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