An instance selection framework for mining data streams to predict antibody-feature function relationships on RV144 HIV vaccine recipients

Data coming from different sources is referred to as data streams. Data stream mining is an online learning technique where each data point must be processed as the data arrives and discarded as the processing is completed. Progress of technologies has resulted in the monitoring these data streams in real time. Data streams has created many new challenges to the researchers in real time. The main features of this type of data are they are fast flowing, large amounts of data which are continuous and growing in nature, and characteristics of data might change in course of time which is termed as concept drift. This chapter addresses the problems in mining data streams with concept drift. Due to which, isolating the correct literature would be a grueling task for researchers and practitioners. This chapter tries to provide a solution as it would be an amalgamation of all techniques used for data stream mining with concept drift.

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Mining Data Streams

Mining of Massive Datasets ◽

10.1017/cbo9781139058452.005 ◽

2012 ◽

pp. 108-138

Author(s):

Anand Rajaraman ◽

Jeffrey David Ullman

Keyword(s):

Data Streams ◽

Mining Data Streams

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Efficient Data Streams Based Closed Frequent Itemsets Mining Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.256-259.2910 ◽

2012 ◽

Vol 256-259 ◽

pp. 2910-2913

Author(s):

Jun Tan

Keyword(s):

Data Streams ◽

Sliding Window ◽

Frequent Itemsets ◽

Streaming Data ◽

Efficient Data ◽

Closed Itemsets ◽

Frequent Itemsets Mining ◽

Synthetic Datasets ◽

Online Mining ◽

Mining Data Streams

Online mining of frequent closed itemsets over streaming data is one of the most important issues in mining data streams. In this paper, we proposed a novel sliding window based algorithm. The algorithm exploits lattice properties to limit the search to frequent close itemsets which share at least one item with the new transaction. Experiments results on synthetic datasets show that our proposed algorithm is both time and space efficient.

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Mining data streams using clustering

2005 International Conference on Machine Learning and Cybernetics ◽

10.1109/icmlc.2005.1527288 ◽

2005 ◽

Author(s):

Yi-Hong Lu ◽

Huang

Keyword(s):

Data Streams ◽

Mining Data Streams

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Mining data streams

ACM SIGMOD Record ◽

10.1145/1083784.1083789 ◽

2005 ◽

Vol 34 (2) ◽

pp. 18-26 ◽

Cited By ~ 541

Author(s):

Mohamed Medhat Gaber ◽

Arkady Zaslavsky ◽

Shonali Krishnaswamy

Keyword(s):

Data Streams ◽

Mining Data Streams

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Mining Data Streams

Data Mining ◽

10.1007/978-3-319-14142-8_12 ◽

2015 ◽

pp. 389-427

Author(s):

Charu C. Aggarwal

Keyword(s):

Data Streams ◽

Mining Data Streams

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Incremental Algorithm for Discovering Frequent Subsequences in Multiple Data Streams

International Journal of Data Warehousing and Mining ◽

10.4018/jdwm.2011100101 ◽

2011 ◽

Vol 7 (4) ◽

pp. 1-20 ◽

Cited By ~ 2

Author(s):

Reem Al-Mulla ◽

Zaher Al Aghbari

Keyword(s):

Data Streams ◽

Data Stream ◽

Clustering Algorithms ◽

Arrival Rate ◽

Time Algorithm ◽

Incremental Algorithm ◽

Multiple Data ◽

Multiple Data Streams ◽

Mining Data Streams ◽

New Applications

In recent years, new applications emerged that produce data streams, such as stock data and sensor networks. Therefore, finding frequent subsequences, or clusters of subsequences, in data streams is an essential task in data mining. Data streams are continuous in nature, unbounded in size and have a high arrival rate. Due to these characteristics, traditional clustering algorithms fail to effectively find clusters in data streams. Thus, an efficient incremental algorithm is proposed to find frequent subsequences in multiple data streams. The described approach for finding frequent subsequences is by clustering subsequences of a data stream. The proposed algorithm uses a window model to buffer the continuous data streams. Further, it does not recompute the clustering results for the whole data stream at every window, but rather it builds on clustering results of previous windows. The proposed approach also employs a decay value for each discovered cluster to determine when to remove old clusters and retain recent ones. In addition, the proposed algorithm is efficient as it scans the data streams once and it is considered an Any-time algorithm since the frequent subsequences are ready at the end of every window.

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