A General Framework for Mining Massive Data Streams

Domingos, Pedro; Hulten, Geoff

doi:10.1201/9781420082333.ch2

Cited by 30 publications

(36 citation statements)

References 5 publications

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“…An extreme condition of this resource-scarced case is the clustering of data streams, where samples must be processed in chunks only once, as they arrive, without being able to access them again. This problem is well understood today, and there are several techniques to solve it such as [1,2,16,26,36,64].…”

Section: Related Workmentioning

confidence: 99%

Warped K-Means: An algorithm to cluster sequentially-distributed data

Leiva

Vidal

2013

Information Sciences

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Many devices generate large amounts of data that follow some sort of sequentiality, e.g., motion sensors, e-pens, eye trackers, etc. and often these data need to be compressed for classification, storage, and/or retrieval tasks. Traditional clustering algorithms can be used for this purpose, but unfortunately they do not cope with the sequential information implicitly embedded in such data. Thus, we revisit the wellknown K-means algorithm and provide a general method to properly cluster sequentially-distributed data. We present Warped K-Means (WKM), a multi-purpose partitional clustering procedure that minimizes the Sum of Squared Error criterion, while imposing a hard sequentiality constraint in the classification step. We illustrate the properties of WKM in three applications, one being the segmentation and classification of human activity. WKM outperformed five state-of-the-art clustering techniques to simplify data trajectories, achieving a recognition accuracy of near 97%, which is an improvement of around 66% over their peers. Moreover, such an improvement came with a reduction in the computational cost of more than one order of magnitude.

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Section: Related Workmentioning

confidence: 99%

Warped K-Means: An algorithm to cluster sequentially-distributed data

Leiva

Vidal

2013

Information Sciences

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“…An interesting, and generalizable, proposal is presented by Domingos and Hulten [39][40][41] whose approach to scaling up learning algorithms is based on Hoeffding bounds [64]. The method can be applied either to choose among a set of discrete models or to estimate a continuous parameter.…”

Section: Using Powerful Search Heuristicsmentioning

confidence: 99%

“…Clustering [39], EM algorithm [40], mining massive data streams [41], mining complex models [67] Improving the efficiency of the search algorithm Minimum spanning trees [94] Algorithm/programming optimization Bookkeeping Decision trees [5] Use of k-d trees Nearest neighbor search [103,132], feature selection [120], clustering [90], classification [134] solve the original problem using all of the available data, which is not simplified or modified in any way. Basically, we try to improve the performance of the algorithm.…”

Section: Modifying the Algorithmmentioning

confidence: 99%

Scaling up data mining algorithms: review and taxonomy

García-Pedrajas

Haro-García

2012

Prog Artif Intell

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The overwhelming amount of data that are now available in any field of research poses new problems for data mining and knowledge discovery methods. Due to this huge amount of data, most of the current data mining algorithms are inapplicable to many real-world problems. Data mining algorithms become ineffective when the problem size becomes very large. In many cases, the demands of the algorithm in terms of the running time are very large, and mining methods cannot be applied when the problem grows. This aspect is closely related to the time complexity of the method. A second problem is linked with performance; although the method might be applicable, the size of the search space prevents an efficient execution, and the resulting solutions are unsatisfactory. Two approaches have been used to deal with this problem: scaling up data mining algorithms and data reduction. However, because data reduction is a data mining task itself, this technique also suffers from scalability problems. Thus, for many problems, especially when dealing with very large datasets, the only way to deal with the aforementioned problems is to scale up the data mining algorithm. Many efforts have been made to obtain methods that can be used to scale up existing data mining algorithms. In this paper, we review the methods that have been used to address the problem of scalability. We focus on general ideas, rather than specific implementations, that can be used to provide a general view of the current approaches for scaling up data mining methods. A taxonomy of the algorithms is proposed, and many examples of different tasks are presented. Among the different techniques used for data mining, we will pay special attention to evolutionary methods, because these methods have been used very successfully in many data mining tasks.

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“…Stream data mining [10] is a topic of active research, and several adaptations of standard statistical and data analysis methods to data streams or related models have been developed recently (e.g. [7,28]).…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Clustering of Parallel Data Streams

Beringer

Hüllermeier

2007

Advances in Fuzzy Clustering and Its Applications

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The management and processing of so-called data streams has recently become a topic of active research in several fields of computer science, notably database systems and data mining. A data stream can roughly be thought of as a transient, continuously increasing sequence of time-stamped data. In this paper, we consider the problem of clustering parallel streams of real-valued data, that is to say, continuously evolving time series. More specifically, we are interested in grouping data streams the evolution over time of which is similar in a specific sense. In order to maintain an up-to-date clustering structure, it is necessary to analyze the incoming data in an online manner, tolerating not more than a constant time delay. For this purpose, we develop an efficient online version of the fuzzy C-means clustering algorithm. A fuzzy approach appears to be particularly useful for this type of application, in which the clustering structure is subject to continuous changes.

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A General Framework for Mining Massive Data Streams

Cited by 30 publications

References 5 publications

Warped K-Means: An algorithm to cluster sequentially-distributed data

Warped K-Means: An algorithm to cluster sequentially-distributed data

Scaling up data mining algorithms: review and taxonomy

Fuzzy Clustering of Parallel Data Streams

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