Results 191 to 200 of about 751,199 (239)

Fuzzy data mining for time-series data

Applied Soft Computing, 2012
Time series analysis has always been an important and interesting research field due to its frequent appearance in different applications. In the past, many approaches based on regression, neural networks and other mathematical models were proposed to analyze the time series.
Chun-Hao Chen, Tzung-Pei Hong, Vincent S. Tseng   +2 more
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Mining Time Series Data

2006
Much of the world’s supply of data is in the form of time series. In the last decade, there has been an explosion of interest in mining time series data. A number of new algorithms have been introduced to classify, cluster, segment, index, discover rules, and detect anomalies/novelties in time series. While these many different techniques used to solve
Chotirat Ann Ralanamahatana, Jessica Lin, Dimitrios Gunopulos, Eamonn Keogh, Michail Vlachos, Gautam Das   +5 more
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Mining from Time Series Human Movement Data

2006 IEEE International Conference on Systems, Man and Cybernetics, 2006
Human motion not only contains a wealth of information about actions and intentions, but also about identity and personal attributes of the moving person. Research also indicates that there are positive relationships between the health of a person and their pattern of motion.
Chiu-Che Tseng, Diane Cook
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Flood prediction using Time Series Data Mining

Journal of Hydrology, 2007
Summary This paper describes a novel approach to river flood prediction using Time Series Data Mining which combines chaos theory and data mining to characterize and predict events in complex, nonperiodic and chaotic time series. Geophysical phenomena, including earthquakes, floods and rainfall, represent a class of nonlinear systems termed chaotic ...
Chaitanya Damle, Ali Yalcin
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Preserving Privacy in Time Series Data Mining

International Journal of Data Warehousing and Mining, 2011
Time series data mining poses new challenges to privacy. Through extensive experiments, the authors find that existing privacy-preserving techniques such as aggregation and adding random noise are insufficient due to privacy attacks such as data flow separation attack.
Ye Zhu, Yongjian Fu, Huirong Fu
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Mining Time Series Data: A Selective Survey

2009
Time series prediction and control may involve the study of massive data archive and require some kind of data mining techniques. In order to make the comparison of time series meaningful, one important question is to decide what similarity means and what features have to be extracted from a time series. This question leads to the fundamental dichotomy:
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Mining temporal classes from time series data

Proceedings of the eleventh international conference on Information and knowledge management - CIKM '02, 2002
In this investigation, we discuss how to mine Temporal Class Schemes to model a collection of time series data. From the viewpoint of temporal data mining, this problem can be seen as discretizing time series data or aggregating them. Also this can be considered as screening (or noise filtering).
Masahiro Motoyoshi, Takao Miura, Kohei Watanabe   +2 more
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Financial Time Series Data Mining

2009
Movement of stocks in the financial market is a typical example of financial time series data. It is generally believed that past performance of a stock can indicate its future trend and so stock trend analysis is a popular activity in the financial community.
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Data representation for time series data mining: time domain approaches

WIREs Computational Statistics, 2016
In most time series data mining, alternate forms of data representation or data preprocessing is required because of the unique characteristics of time series, such as high dimension (the number of data points), presence of random noise, and nonlinear relationship of the data elements.
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Using sonification for mining time series data

Proceedings of the 9th International Workshop on Multimedia Data Mining: held in conjunction with the ACM SIGKDD 2008, 2008
In recent years, there is a growing interest in mining time series databases by both automated and interactive tools. In this paper, we present an interactive methodology for mining of time series data using a novel sonification technique which uses some important properties of time series and tonal music to achieve effective (accurate) and efficient ...
Mark Last, Anna Gorelik
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