Search Results - (( data deviation detection algorithm ) OR ( java application mining algorithm ))
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Direct approach for mining association rules from structured XML data
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Sensing texture using an artificial finger and a data analysis based on the standard deviation
Published 2015“…It windows the data, calculates the standard deviation of the data in the windows and then averages the standard deviations. …”
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Deviation detection in text using conceptual graph interchange format and error tolerance dissimilarity function
Published 2012“…The rapid increase in the amount of textual data has brought forward a growing research interest towards mining text to detect deviations. …”
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Study and Implementation of Data Mining in Urban Gardening
Published 2019“…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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Sensing texture using an artificial finger and a data analysis based on the standard deviation
Published 2015“…For the automatic control of an artificial hand and to feedback information to the amputee, an algorithm has been developed based on the standard deviation (SD) of signal data from the sensor. …”
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Fault diagnostic algorithm for precut fractionation column
Published 2004“…Hazard and Operability Study (HAZOP) is used to support the diagnosis task. The algorithm has been successful in detecting the deviations of each variable by testing the data set. …”
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Autonomous anomaly detection using density-based features in streaming data / Muhammmad Yunus Iqbal Basheer
Published 2023“…Hence, it is critical for an anomaly detection algorithm to detect data anomalies patterns. …”
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9
Development of a rule-based fault diagnostic advisory system for precut fractionation column
Published 2005“…The system detected and diagnosed process deviations using the saved data and the set limit. …”
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10
Algorithm enhancement for host-based intrusion detection system using discriminant analysis
Published 2004“…Anomaly detection algorithms model normal behavior. Anomaly detection models compare sensor data to normal patterns learned from the training data by using statistical method and try to detect activity that deviates from normal activity. …”
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Mining Sequential Patterns Using I-PrefixSpan
Published 2007“…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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The effect of different distance measures in detecting outliers using clustering-based algorithm for circular regression model
Published 2017“…Outlier detection in linear data sets has been done vigorously but only a small amount of work has been done for outlier detection in circular data. …”
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The multiple outliers detection for circular univariate data using different agglomerative clustering algorithms
Published 2024“…This study proposes the procedure of detecting multiple outliers, particularly for univariate circular data based on agglomerative clustering algorithms. …”
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A web-based implementation of k-means algorithms
Published 2022“…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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Final Year Project / Dissertation / Thesis -
15
A study on advanced statistical analysis for network anomaly detection
Published 2005“…Anomaly detection algorithms model normal behavior. Anomaly detection models compare sensor data to normal patterns learned from the training data by using statistical method and try to detect activity that deviates from normal activity. …”
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Monograph -
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Mining Sequential Patterns using I-PrefixSpan
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An enhancement of classification technique based on rough set theory for intrusion detection system application
Published 2019“…An Intrusion Detection System (IDS) is capable to detect unauthorized intrusions into computer systems and networks by looking for signatures of known attacks or deviations of normal activity. …”
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Thesis -
18
A Review of Unsupervised Machine Learning Frameworks for Anomaly Detection in Industrial Applications
Published 2022“…Anomaly detection, sometimes referred to as outlier analysis is a data mining procedure that detects events, data points, and observations that deviates from the expected behaviour of a dataset. …”
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Comparative study of clustering-based outliers detection methods in circular-circular regression model
Published 2021“…This paper is a comparative study of several algorithms for detecting multiple outliers in circular-circular regression model based on the clustering algorithms. …”
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Comparative study of clustering-based outliers detection methods in circular-circular regression model
Published 2021“…This paper is a comparative study of several algorithms for detecting multiple outliers in circular-circular regression model based on the clustering algorithms. …”
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