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Similarity measure is a central problem in time series data mining. Although most approaches to this problem have been developed, with the rapid growth of the amount of data, we believe there is a challenging demand for supporting similarity measure in a fast and accurate way. In this paper, we propose a new time series representation model and a corresponding similarity measure, which is able to...
Data representation and similarity measurement are two basic aspects of similarity detection in time series data mining. In this paper, we present two novel approaches to perform similarity detection efficiently and effectively. One is composed of a new time series representation model and a corresponding similarity measure, which is called fragment alignment distance (FAD); the other applies dynamic...
Mining abnormal patterns is important in many areas. With the prevalence of big data, in order to ensure efficiency, an algorithm named PPSpan (JOMP-based parallel Prefix Span) is proposed under the research of traditional serial sequential pattern mining methods. Firstly, redundant parameters are eliminated with grey correlation analysis. Secondly, outlier information is extracted according to the...
Traditional k-means algorithm cannot get high clustering precise rate, and easily be affected by clustering center random initialized and isolated points, but the algorithm is simple with low time complexity, and can process the big data set quickly. This paper proposes an improved k-means algorithm named PKM. PKM is based on similarity degree among data points made by cumulated K-means, and get the...
Trajectory clustering is attractive for the task of class identification in spatial database. Existing trajectory clustering algorithm TRCLUS uses global parameters to discover common trajectories. However, it can not discover small and dense clusters and be sensitive to two input parameters. Based on the partition-and-group framework, we propose a simple but effective trajectory clustering algorithm...
The study on streaming data is one of the hot topics among the database field recently. Unlike traditional data sets, stream data arrive continuously and they are fast changing, massive, possibly unpredictable. These characteristics of data stream determine that only approximate queries on them are proper. The key of approximate query is to construct a synopsis data structure far smaller than the...
Clustering is an important task in data mining with numerous applications, including minefield detection, seismology, astronomy, etc. At present, the academic communities have introduced various clustering algorithms, and these methods have been widely applied to different fields according to their respective characteristics. In this paper, we propose a novel clustering algorithm based on symmetric...
Density-based clustering and density-based outlier detection have been extensively studied in the data mining. However, Existing works address density-based clustering or density-based outlier detection solely. But for many scenarios, it is more meaningful to unify density-based clustering and outlier detection when both the clustering and outlier detection results are needed simultaneously. In this...
Existing trajectory clustering algorithm TRACLUS uses global parameters, it can not distinguish small, close, and dense trajectory clusters from large and sparse trajectory clusters. Moreover, TRACLUS needs two input parameters and is sensitive to input parameters. To avoid the shortcomings of TRACLUS, a neighborhood-based trajectory clustering algorithm named NBTC is proposed based on the improved...
This paper firstly analyzes the reason why data mining should be implemented in CIM (computer integrated manufacturing) system. Secondly current developments about data mining frameworks are introduced. CIMSMiner is a framework for cooperative manufacturing, which combines the practical requirements of CIM system with the new evolution of data mining. The logical architecture, system objectives and...
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