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

The paper proposes a novel methodology of finding frequent itemsets in data stream. Fuzzification of support of closed frequent itemsets in conjunction with jumping window has been used for finding frequent itemsets. Fuzzification of support of closed frequent itemsets helps in preserving information regarding the frequent itemsets at different point in time in the data stream. Use of jumping window over the high speed data stream improves the speed of the proposed algorithm. Effectiveness of the proposed algorithm is shown by comparing its performance with the widely known MOMENT algorithm on both IBM synthetic datasets and benchmark datasets taken from UCI Machine Learning Repository.

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“…Liu, Guan and Hu [10] proposed FP-CDS algorithm uses FP-CDS tree for storing closed frequent itemsets over landmark window.…”

Section: Overview Of the Earlier Workmentioning

confidence: 99%

“…Data stream algorithms are broadly divided into three categories on the basis of the type of window used viz. landmark [10] [11]; damped [5] [7] [8]; sliding [4] [12].…”

Section: Introductionmentioning

confidence: 99%

A novel approach of finding frequent itemsets in high speed data streams

Chandra

Bhaskar

2011

2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

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“…Recently, data streams have been extensively investigated due to the large amount of applications such as sensor networks, web click streams and network flows. [1][2][3][4][5][6] Data stream is an ordered sequence of data with huge volumes arriving at a high throughput that must be analyzed in a single pass. [7][8][9] Vast majority of researches in the context of data stream mining are devoted to supervise learning, whereas, in real word human practice label of data are rarely available to the learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

A statistical approach for clustering in streaming data

Mozafari

Hashemi

Hamzeh³

2014

AIR

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Recently data stream has been extensively explored due to its emergence in large deal of applications such as sensor networks, web click streams and network flows. Vast majority of researches in the context of data stream mining are devoted to supervise learning, whereas, in real word human practice label of data are rarely available to the learning algorithms. Hence, clustering as the most important unsupervised learning has been in the gravity of focus of quite a lot number of the researchers in data stream community. Clustering paradigms basically place the similar objects together and separate the dissimilar ones into different clusters. In this paper, we propose a Statistical framework for data Stream Clustering, which abbreviated as StatisStreamClust that makes use of two components to find clusters in data stream. The first component especially designed to detect concept change where data underlying distributions change from time to time. Upon detection of concept change by the first component, the second component is triggered to update the whole clustering model. StatisStreamClust brings great benefits to data stream clustering including no sensitivity to the number of clusters and dimensions, reasonable complexity and in the meantime desirable performance, and finally no need to determine window size a priori. To explore the advantages of our approach, quite a lot of experiments with different settings and specifications are conducted. The obtained results are very promising.

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“…Data stream mining algorithms are broadly divided into three categories on the basis of type of window used, viz. landmark, damped, and sliding …”

Section: Introductionmentioning

confidence: 99%

“…The MFI-TransSW algorithm proposed by Li et al 12 uses a transaction sensitive sliding window for finding frequent itemsets in the data stream by using bit sequence representation of items. Liu et al 5 proposed the (frequent pattern-closed data stream (FP-CDS) algorithm that uses the FP-CDS tree for storing closed frequent itemsets over landmark window.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Finding Frequent Itemsets in Data Stream

Chandra

Bhaskar

2013

Int. J. Intell. Syst.

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The paper proposes a novel methodology of finding frequent itemsets in data stream. Fuzzification of support of the closed frequent itemsets in conjunction with a jumping window has been used for finding frequent itemsets. Closed frequent itemsets help in retaining all frequent itemsets in a reduced memory space. Fuzzifying the support of the closed frequent itemsets helps in preserving information regarding frequent itemsets at different time intervals in the data stream. The use of the jumping window over the data stream improves the speed of the proposed algorithm. Effectiveness of the proposed algorithm in terms of execution time and accuracy is shown by comparing its performance with the widely known MOMENT algorithm on both IBM synthetic data sets and benchmark data sets taken from the UCI Machine Learning Repository.

show abstract

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

Cited by 34 publications

References 12 publications

A novel approach of finding frequent itemsets in high speed data streams

A novel approach of finding frequent itemsets in high speed data streams

A statistical approach for clustering in streaming data

A Novel Approach for Finding Frequent Itemsets in Data Stream

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