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mining time series data

Mining previously unknown patterns in time series data

Mining previously unknown patterns in time series data

... n data elements, a partition-based clustering method assign each data element into k groups where k ≤ n so that each group contains at least one ...each data element is then allocated to its nearest ...

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Ownership protection of outsourced biomedical time series data based on optimal watermarking scheme in data mining

Ownership protection of outsourced biomedical time series data based on optimal watermarking scheme in data mining

... biomedical data in data mining has been ...biomedical data with different characteristics for medical ...biomedical data and ...biomedical data via network with ownership ...

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A New Similarity Measure for Time Series Data Mining Based on Longest Common Subsequence

A New Similarity Measure for Time Series Data Mining Based on Longest Common Subsequence

... of time series based on logic and characteristics of the LCSS method is presented which uses two similarity thresholds that named Developed Longest Common Subsequence ...assigning time series ...

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Text‑Mining in Streams of Textual Data Using Time Series Applied to Stock Market

Text‑Mining in Streams of Textual Data Using Time Series Applied to Stock Market

... next data point not included in the newly approximated ...first data point of a time series, then attempting to approximate the data to the right with increasing longer ...entire ...

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Finding kernel function for stock market prediction with support vector regression

Finding kernel function for stock market prediction with support vector regression

... Stock data, time series modeling and stock prediction, data mining operations and techniques and lastly support vector machine and artificial neural ...the data collection, ...

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													Comparative study of different data mining prediction algorithms

1. Comparative study of different data mining prediction algorithms

... In time series models, historic data is used to generate trends for the ...Statistical mining models are used to determine the statistical validity of test parameters and can be utilized to ...

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Time Series Data Mining in Real Time Surface Runoff Forecasting through Support Vector Machine

Time Series Data Mining in Real Time Surface Runoff Forecasting through Support Vector Machine

... The idea of support vector machine is initially proposed by Vapnik and his co-workers in late 60’s. SVMs are effective machine learning system which can be used in pattern classification, multiphase evaluation; ...

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Financial Trading System using Combination of Textual and Numerical Data

Financial Trading System using Combination of Textual and Numerical Data

... financial data that are generated and evaluated at a high ...financial data is coming continuously, changing with time and may be ...of data to support companies and individuals for ...

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Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

... level data entities including different measurements of the object under analysis (for example, a patient EEG) usually depend on the central ...long time after the last seizure). The data collected ...

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Data Mining Algorithm for Off-Group Points on Noise Polluted Time Series Based on ESO

Data Mining Algorithm for Off-Group Points on Noise Polluted Time Series Based on ESO

... Abstract – The practically measured signals always contain wild values deviating far from true values. How to remove these wild values is an important research project for data mining of off-group points. ...

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Pattern Discovery in Hydrological Time Series Data Mining during the Monsoon Period of the High Flood Years in Brahmaputra River Basin

Pattern Discovery in Hydrological Time Series Data Mining during the Monsoon Period of the High Flood Years in Brahmaputra River Basin

... rule mining, thus creating many new algorithms for ...of time series analysis: (i) identifying the nature of the phenomenon represented by the sequence of observations, and (ii) forecasting ...

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Online Full Text

Online Full Text

... with time (monthly or yearly) were analyzed using time series ...perform time series analysis using Autoregressive tree model (ART) ...in time series data ...for ...

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Crime Patterns and Prediction: A Data Mining and Machine Learning Approach

Crime Patterns and Prediction: A Data Mining and Machine Learning Approach

... Time series analysis can be defined as the method for examining and scrutinizing the time series data so that meaningful statistics and useful information can be extracted from ...

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Real Time Prediction System of Discharge of the Rivers using Clustering Technique of Data Mining

Real Time Prediction System of Discharge of the Rivers using Clustering Technique of Data Mining

... Multivariate Time Series (MTS) similarity mining has been ...Subsequence time series (STS) Clustering Techniques for meaningful pattern discovery has been discovered ...historical ...

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Applied temporal Rule Mining to Time Series

Applied temporal Rule Mining to Time Series

... rule mining framework we used the so called Cylinder-Bell-Funnel data set [18, 13, ...a time series by con- catenating 90 instances of the cylinder (C), bell (B) and funnel (F) patterns using ...

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Using Data Mining with Time Series Data in Short Term Stocks Prediction: A Literature Review

Using Data Mining with Time Series Data in Short Term Stocks Prediction: A Literature Review

... with time series data is popular and many applica- tions can be found in the literature, for instance, for earthquake forecasting [12], characterization of ozone behavior [13], or flood prediction ...

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Development of a Modelling Script of Time Series Suitable for Data Mining

Development of a Modelling Script of Time Series Suitable for Data Mining

... Data Mining has become an important technique for the exploration and extraction of data in nu- merous and various research projects in different fields (technology, information technology, business, ...

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Fault prediction of fan bearing using time series data mining

Fault prediction of fan bearing using time series data mining

... on time series prediction. Typical time series prediction methods generally use linear model to approximate data series, and they are only effective for fault prediction in ...

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Efficient Periodicity Mining using Circular Autocorrelation in Time Series Data

Efficient Periodicity Mining using Circular Autocorrelation in Time Series Data

... Basically, our approach guide to detect periodic pattern without prior knowledge of nature of data. The algorithm based on basic filtering step by computing circular autocorrelation using fast fourier transform. ...

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Transition in Time Series Data Mining on Correlated Items

Transition in Time Series Data Mining on Correlated Items

... mining [13][14][15]. A sequential pattern, defined first by Agrawal and Srikant [13], is a sequence of elements whose occurrence frequency in a set of sequences (called a sequence database) is not less than a ...

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