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DSM-FI: an efficient algorithm for mining frequent itemsets in data streams

Hua-Fu Li, Man-Kwan Shan, Suh-Yin Lee
2008 Knowledge and Information Systems  
of one-pass mining of frequent itemsets.  ...  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  ...  In this paper, we will focus on the problem of mining frequent itemsets in data streams.  ... 
doi:10.1007/s10115-007-0112-4 fatcat:v4ildu6wkbfp3eqdhw2h54ke74

Comparative evaluation of pattern mining techniques: an empirical study

Anindita Borah, Bhabesh Nath
2020 Complex & Intelligent Systems  
The paper provides a structural classification of the widely referenced techniques in four pattern mining categories: frequent, maximal frequent, closed frequent and rare.  ...  The results illustrate that tree based approaches perform exceptionally well over level wise approaches in case of dense data sets for all the categories.  ...  One of the challenges in the field of maximal frequent pattern mining is the generation of maximal frequent patterns from data streams.  ... 
doi:10.1007/s40747-020-00226-4 fatcat:jrn2z6rrcvdjhccdboitf5s4jq

Mining frequent closed itemsets from a landmark window over online data streams

Xuejun Liu, Jihong Guan, Ping Hu
2009 Computers and Mathematics with Applications  
Potential frequent closed itemsets in each basic window are mined and stored in FP-CDS tree based on some proposed strategies. Extensive experiments are conducted to validate the proposed method.  ...  However, mining frequent closed itemsets from a landmark window over data streams is a challenging problem.  ...  propose an algorithm to find maximal frequent itemsets from data streams. Lin et al.  ... 
doi:10.1016/j.camwa.2008.10.060 fatcat:bxztp347bvdbja5g6asgcdyut4

SuffixMiner: Efficiently Mining Frequent Itemsets in Data Streams by Suffix-Forest [chapter]

Lifeng Jia, Chunguang Zhou, Zhe Wang, Xiujuan Xu
2005 Lecture Notes in Computer Science  
property of suffixtree to avoid generating candidate itemsets and traversing each suffix-tree during the itemset growth, and utilizes a new itemset growth method to mine all frequent itemsets in data streams  ...  Experiment results show that the Suffix-Miner algorithm not only has an excellent scalability to mine frequent itemsets over data streams, but also outperforms Apriori and Fp-Growth algorithms.  ...  [3] developed a FP-tree-based algorithm, called FP-stream, to mine frequent itemsets at multiple time granularities by a novel titled-time windows technique.  ... 
doi:10.1007/11540007_72 fatcat:hzb2bwrypvavpc5zkf35iwgipu

Frequent Pattern Mining Algorithms for Finding Associated Frequent Patterns for Data Streams: A Survey

Shamila Nasreen, Muhammad Awais Azam, Khurram Shehzad, Usman Naeem, Mustansar Ali Ghazanfar
2014 Procedia Computer Science  
and Associated Sensor Pattern Mining of Data Stream (ASPMS) frequent pattern mining algorithms.  ...  This has been presented in the form of a comparative study of the following algorithms: Apriori algorithm, Frequent Pattern (FP) Growth algorithm, Rapid Association Rule Mining (RARM), ECLAT algorithm  ...  Conditional FP tree base and Conditional FP tree are based on node link property and prefix path property. Conditional pattern base for each element in head table is shown in Fig 4(a) .  ... 
doi:10.1016/j.procs.2014.08.019 fatcat:ngskhuc6ffa3pfwsu7dv7fctce

Search Method of Time Sensitive Frequent Itemsets in Data Streams [chapter]

Tae-Su Park, Ju-Hong Lee, Sang-Ho Park, Bumghi Choi, Deok-Hwan Kim
2006 Lecture Notes in Computer Science  
In this paper we propose a novel algorithm for finding the relative frequent itemsets according to the time in a data stream.  ...  Therefore, the itemsets which were not the frequent itemsets can become frequent itemsets. The volume of data stream is so large that it can hardly be stored in finite memory space.  ...  Conclusion One of the most fundamental problems in data streams is how to search frequent itemsets generated from the stream.  ... 
doi:10.1007/11892755_53 fatcat:thzohozvonaevpi7i5leqx263e

Mining Frequent Itemsets with Normalized Weight in Continuous Data Streams

Young-Hee Kim, Won-Young Kim, Ung-Mo Kim
2010 Journal of Information Processing Systems  
In this paper, we present an efficient algorithm WSFI (Weighted Support Frequent Itemsets)-Mine with normalized weight over data streams.  ...  In many application areas, mining frequent itemsets has been suggested to find important frequent itemsets by considering the weight of itemsets.  ...  Construction of a WSFP-Tree ensures that frequent pattern mining can be performed efficiently. A WSFP-Tree is a data structure based on an extended FP-tree.  ... 
doi:10.3745/jips.2010.6.1.079 fatcat:w6af4fxchbcc5jbhwxt7jsi7yq

Mining of Frequent Itemsets from Streams of Uncertain Data

Carson Kai-Sang Leung, Boyu Hao
2009 Proceedings / International Conference on Data Engineering  
To deal with these situations, we propose two tree-based mining algorithms to efficiently find frequent itemsets from streams of uncertain data, where each item in the transactions in the streams is associated  ...  Experimental results show the effectiveness of our algorithms in mining frequent itemsets from streams of uncertain data.  ...  For example, Mining static databases Mining dynamic data streams Apriori-based Tree-based (tree-based) Mining precise data Apriori [2] FP-growth [10] FP-streaming [9] Mining uncertain data U-Apriori  ... 
doi:10.1109/icde.2009.157 dblp:conf/icde/LeungH09 fatcat:e7ckhh5klve5xfj6ttaiz5lxk4

A review and analysis on knowledge discovery and data mining techniques

Bhagawan Singh, Vivek Dubey
2018 International Journal of Advanced Technology and Engineering Exploration  
example bases and contingent example tree during Review Article Abstract Data mining is used for the knowledge discovery in the area of engineering, medical diagnosis, business analytics, etc.  ...  The FP-development calculation is quicker than the Apriori algorithm around a request of extent, the recursive FPgrowth calculation for mining incessant itemsets needs to over and over build restrictive  ...  previous literature.  There is the need of exploration in mining weighted frequent itemsets in the data streams.  ... 
doi:10.19101/ijatee.2018.541006 fatcat:xctz44fa75hotj7dpagsxpomsy

Fast algorithms for frequent itemset mining using FP-trees

G. Grahne, J. Zhu
2005 IEEE Transactions on Knowledge and Data Engineering  
Methods for mining frequent itemsets have been implemented using a prefix-tree structure, known as an FP-tree, for storing compressed information about frequent itemsets.  ...  Our technique works especially well for sparse data sets. Furthermore, we present new algorithms for mining all, maximal, and closed frequent itemsets.  ...  Based on the correctness of the FPmax method, we can conclude that FPmax* returns all and only the maximal frequent itemsets in a given data set.  ... 
doi:10.1109/tkde.2005.166 fatcat:rrz7lktr7rdo5naa5pbwppiopy

Dynamic Support Range based Rare Pattern Mining over Data Streams

Sunitha Vanamala, L. Padma Sree, S. Durga Bhavani
2022 International Journal of Advanced Computer Science and Applications  
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.  ...  To save time and money, pruning is used in RP-Tree [21] . Patterns based on FP-Tree are stored in a Tree based hierarchical file system.  ... 
doi:10.14569/ijacsa.2022.0130378 fatcat:dmwwofdlbza7nbi7ujmgn2wqfu

Anomalous Payload Detection System Using Analysis of Frequent Sequential Pattern

Jun Ma, Guanzhong Dai, Jing Zhou
2009 2009 Fifth International Conference on Information Assurance and Security  
In this work, the practical problem of frequent-itemset discovery in data-stream environments which may suffer from data overload.  ...  The DBCA algorithm extracts base information from data streams in a dynamic way.  ...  It performs the mining task by approximating the counts of longer itemsets (based on the monitored itemsets) and returning the frequent ones as the mining outcome.  ... 
doi:10.1109/ias.2009.34 dblp:conf/IEEEias/MaDZ09 fatcat:pbxxfwow5vbm5lh6cthytvxifi

Frequent Pattern Retrieval on Data Streams by using Sliding Window

P. Kumar, P. Rao
2021 EAI Endorsed Transactions on Energy Web  
In this paper, the Frequent Pattern Retrieval strategy is planned by utilizing an FP tree approach and a sliding window model to extract noteworthy examples from data streams.  ...  In this examination, the sliding window is utilized to build the framework and FP tree applied to mine the dataset's valuable data.  ...  Q and X Wang [26] developed Parallel Mining Collaborative frequent itemsets in Multiple Data stream (PMCMD-Stream).  ... 
doi:10.4108/eai.13-1-2021.168091 fatcat:ud3pbbgmq5gl3b525spburyl7i

RFIMiner: A regression-based algorithm for recently frequent patterns in multiple time granularity data streams

Lifeng Jia, Zhe Wang, Nan Lu, Xiujuan Xu, Dongbin Zhou, Yan Wang
2007 Applied Mathematics and Computation  
Besides the estimation mechanism, many other literatures present some ingenious technologies related with mining frequent itemsets in the data stream. , a FP-tree-based algorithm.  ...  Based on the discussion so far, the single scan requirement of streaming data model conflicts with the objective of frequent itemset mining which is to discovery the complete set of frequent itemsets.  ...  Mining frequent itemsets in the data stream requires devised algorithms to be competent in the scalability. A doubt might be proposed based on the description concerning suffix-trees.  ... 
doi:10.1016/j.amc.2006.06.115 fatcat:45kiaxjat5dsxgm6wne6gzpa4u

Mining Recent Frequent Itemsets in Data Streams by Radioactively Attenuating Strategy [chapter]

Lifeng Jia, Zhe Wang, Chunguang Zhou, Xiujuan Xu
2005 Lecture Notes in Computer Science  
First, it is a single-scan algorithm which utilizes the special property of suffix-trees to guarantee that all frequent itemsets are mined.  ...  We propose a novel approach for mining recent frequent itemsets. The approach has three key contributions.  ...  Introduction Mining frequent itemsets is an essential data mining operation in many data mining problems such as mining association rules, sequential patterns, closed patterns, and maximal pattern.  ... 
doi:10.1007/11527503_95 fatcat:isfj72d5uje5ziqjqussxxpn3u
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