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Mining top-K frequent itemsets from data streams

Raymond Chi-Wing Wong, Ada Wai-Chee Fu
2006 Data mining and knowledge discovery  
We study the problem of mining top K frequent itemsets in data streams. We introduce a method based on the Chernoff bound with a guarantee of the output quality and also a bound on the memory usage.  ...  Frequent pattern mining on data streams is of interest recently. However, it is not easy for users to determine a proper frequency threshold.  ...  Conclusion In this paper, we study the problem of mining the K most frequent itemsets (or top-K itemsets) from data streams.  ... 
doi:10.1007/s10618-006-0042-x fatcat:kgisynyoa5cl3dbue4hnl4dbde

Hardware Acceleration of Frequent Itemsets Mining on Data Streams

Lázaro Bustio-Martínez, René Cumplido-Parra, Raudel Hernández-León, Claudia Feregrino-Uribe
2014 Research in Computing Science  
In data streams mining, the detection of topk frequent 1−itemsets can be seen as a preprocessing stage.  ...  Top-k Frequent 1-Itemsets Detection Data streams are data sources that evolve over time.  ...  A new know-how for Frequent Itemsets Mining in data streams. 4.  ... 
doi:10.13053/rcs-71-1-2 fatcat:dflwgkbs6re77fhancp23ymetm

Mining Top-KItemsets over a Sliding Window Based on Zipfian Distribution [chapter]

Raymond Chi-Wing Wong, Ada Wai-Chee Fu
2005 Proceedings of the 2005 SIAM International Conference on Data Mining  
In this paper, we adopt this model to mine the K most interesting itemsets, or to estimate the K most frequent itemsets of different sizes in a data stream.  ...  Frequent pattern discovery in data streams can be very useful in different applications. In time critical applications, a sliding window model is needed to discount stale data.  ...  Conclusion In this paper, we address the problem of mining the K most frequent itemsets in a sliding window in a data stream. We propose an algorithm to estimate these K itemsets in the data stream.  ... 
doi:10.1137/1.9781611972757.52 dblp:conf/sdm/WongF05 fatcat:glq6obh4ijaofpnwub6mtiwdpy

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  
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  ...  Finally, the set of all frequent itemsets is determined from the current SFI-forest.  ...  There are still many interesting research issues related to the extensions of DSM-FI algorithm, such as mining dynamic data streams, mining top-k frequent itemsets in streaming data, and mining constraint-based  ... 
doi:10.1007/s10115-007-0112-4 fatcat:v4ildu6wkbfp3eqdhw2h54ke74

Mining top-k high utility patterns over data streams

Morteza Zihayat, Aijun An
2014 Information Sciences  
The contributions of the paper are as follows: We are the first to propose a method for mining top-k high utility itemsets from data streams.  ...  To the best of our knowledge, existing methods for mining HUIs over data streams do not address the issue of mining top-k HUIs, and previous top-k HUI mining methods do not work on data streams.  ...  In frequent itemsets mining, several methods were proposed to find top-k frequent itemsets in static data sets [8, 9, 15, 27] .  ... 
doi:10.1016/j.ins.2014.01.045 fatcat:xhdukofadzhfpmyoryisx6uu2q

An Efficient Outlier Detection Approach Over Uncertain Data Stream Based on Frequent Itemset Mining

Shangbo Hao, Saihua Cai, Ruizhi Sun, Sicong Li
2019 Information Technology and Control  
Then, the "upper cap" concept is used in the FIM-UDS method to mine the frequent itemsets more effectively to support outlier detection.  ...  Outlier detection is essential in data-based science. It aims to detect those itemsets that have a significant difference from the other data.  ...  Frequent Itemset Mining on Data Stream In recent years, several frequent itemset mining algorithms have been proposed to mine frequent itemsets on data stream.  ... 
doi:10.5755/j01.itc.48.1.21162 fatcat:2szgvkpxabc3znooquicvp5ofm

Survey on Algorithms for High Utility Itemset Generation

Prof Jyoti B. Kulkarni, Aishwarya Dingre, Sonal Bhosale, Deepali Mehroliya, Shivani Mudgal
2017 IJARCCE  
Data mining is becoming a popular research topic with its frequent applications in online ebusiness, web click stream analysis and cross marketing.  ...  Mining high utility itemsets from a transactional database is concerned with the discovery of itemsets with high utilities like profits or gains.  ...  itemsets with negative item profits from data streams.  ... 
doi:10.17148/ijarcce.2017.6322 fatcat:az2sdhonebcwlbzr66f3zqulhq

An Algorithm of Top-k High Utility Itemsets Mining over Data Stream

Tianjun Lu, Yang Liu, Le Wang
2014 Journal of Software  
Index Terms-data stream, high utility itemset, frequent itemset, data mining, top-k  ...  Existing top-k high utility itemset (HUI) mining algorithms generate candidate itemsets in the mining process; their time & space performance might be severely affected when the dataset is large or contains  ...  The problem of mining top-k HUIs over data stream based on sliding window model is in fact a problem of mining top-k HUIs from the current window; it can be divided into the following two tasks: (1) maintain  ... 
doi:10.4304/jsw.9.9.2342-2347 fatcat:lrw5tabpm5ekxdg2cfebrumvfq

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  
Second, we develop RFIMiner, a single-scan algorithm for mining recently frequent patterns from data streams.  ...  Besides querying data streams, another important task is to mine data streams for frequent itemsets.  ...  Mining frequent itemsets by suffix-trees To mining frequent itemsets, an efficient data structure must be employed to store the summary information concerning transactions in the data stream.  ... 
doi:10.1016/j.amc.2006.06.115 fatcat:45kiaxjat5dsxgm6wne6gzpa4u

A general streaming algorithm for pattern discovery

Debprakash Patnaik, Srivatsan Laxman, Badrish Chandramouli, Naren Ramakrishnan
2013 Knowledge and Information Systems  
Discovering frequent patterns over event sequences is an important data mining problem.  ...  We present the first streaming algorithm for mining frequent patterns over a window of recent events in the stream.  ...  Both can be downloaded from the Frequent Itemset Mining Dataset Repository  ... 
doi:10.1007/s10115-013-0669-z fatcat:64lmgrbkmrbblkdpxvzxj2ipgi

Efficient Maintenance and Mining of Frequent Itemsets over Online Data Streams with a Sliding Window

Hua-Fu Li, Chin-Chuan Ho, Man-Kwan Shan, Suh-Yin Lee
2006 2006 IEEE International Conference on Systems, Man and Cybernetics  
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.  ...  Wong and Fu [18] proposed an efficient algorithm to mine top-k frequent itemsets in offline data streams with a transaction-sensitive sliding window without a user-defined minimum support constraint.  ... 
doi:10.1109/icsmc.2006.385267 dblp:conf/smc/LiHSL06 fatcat:d3wcu67yk5bfzcwxqtlwehnjei

Mining frequent itemsets over data streams using efficient window sliding techniques

Hua-Fu Li, Suh-Yin Lee
2009 Expert systems with applications  
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 from data streams within a transaction-sensitive sliding window which consists of a fixed number of transactions.  ...  , such as SWF, Moment and SWFI-stream, for mining frequent itemsets from data streams within a sliding window.  ... 
doi:10.1016/j.eswa.2007.11.061 fatcat:z5oduyhsfvfcljtw5mafnwkejy

Resource-oriented approximation for frequent itemset mining from bursty data streams

Yoshitaka Yamamoto, Koji Iwanuma, Shoshi Fukuda
2014 Proceedings of the 2014 ACM SIGMOD international conference on Management of data - SIGMOD '14  
This study considers approximation techniques for frequent itemset mining from data streams (FIM-DS) under resource constraints.  ...  ., itemsets) to be generated from each streaming transaction and stored in memory. Various types of approximation methods have been proposed for FIM-DS.  ...  To the best of our knowledge, the first constraint has not yet been introduced in online (top-k) itemset mining, though previous work has examined online top-k item mining [14] and offline top-k itemset  ... 
doi:10.1145/2588555.2612171 dblp:conf/sigmod/YamamotoIF14 fatcat:7a3jjpsn7zfiden6msylmnuz5q

Mining Top-K Click Stream Sequences Patterns

MEHDI Haj Ali, Qun-Xiong Zhu, Yan-Lin He
2016 Indonesian Journal of Electrical Engineering and Computer Science  
As a solution, we developed an efficient algorithm, called TopK (Top-K click stream sequence pattern mining), which employs the output as top-k patterns , K is the most important and relevant frequencies  ...  <p>Sequential pattern mining, it is not just important in data mining field , but it is the basis of many applications .However, running applications cost time and memory, especially when dealing with  ...  The definition of this problem is similar to the definition of other top-k problems in the field of pattern mining such as top-k frequent itemset mining [1] , [11] , [14] , top-k association rule mining  ... 
doi:10.11591/ijeecs.v4.i3.pp655-664 fatcat:uhtd7pcrejcjbckpzmis3jqmam

An Efficient Mining Algorithm by Bit Vector Table for Frequent Closed Itemsets

Keming Tang, Caiyan Dai, Ling Chen
2011 Journal of Software  
Mining frequent closed itemsets in data streams is an important task in stream data mining.  ...  In this paper, an efficient mining algorithm (denoted as EMAFCI) for frequent closed itemsets in data stream is proposed.  ...  INTRODUCTION Mining frequent itemsets from data streams is an important problem with wide applications in data streams analysis.  ... 
doi:10.4304/jsw.6.11.2121-2128 fatcat:tgfjld6tozcfxmahjtrvt5plhy
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