[PDF] Top 20 Outlier Detection Technique in Data Mining: A Research Perspective
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Outlier Detection Technique in Data Mining: A Research Perspective
... stronger outlier than a point with more neighboring within a distance ...for mining outliers which the user dose not need to specify the distance parameter ... See full document
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A comparative study for outlier detection techniques in data mining
... in data mining mostly focus on finding patterns in large datasets and further using it for organizational decision ...the data mining field as some other topics have, such as association ... See full document
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Application of data mining techniques for outlier mining in medical databases
... Statistical outlier detection techniques are essentially model- based techniques; ...the data, and the data instances are evaluated with respect to how well they fit the model ...a data ... See full document
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Outlier Detection Approaches in Data Mining
... — Outlier is defined as an event that deviates too much from other ...of outlier can lead to the discovery of useful and meaningful ...knowledge. Outlier means it’s happen at some time it’s not ... See full document
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An Improvement in Outlier Detection Using Fuzzy Eclarance Clustering Algorithm for Data Mining
... fuzzy technique that minimizing root mean square error approximately 900 iterations (means maximum number of iteration when data point get large) having ...in outlier detection ...in ... See full document
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EVALUATION OF OUTLIER DETECTION FOR TRAJECTORY DATA
... three perspective, firstly algorithm considering multi- attribute, secondly, suitable distance metric, and thirdly other studies attempt to improve existing algorithm is presented in ...of outlier ... See full document
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1. Spatial data mining for finding nearest neighbor and outlier detection
... Ian De Felipe [13] proposed an algorithm using R-tree used to find the objects closer to query location and that contains a set of keywords also. These keywords include the attributes of the objects. In spatial ... See full document
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Outlier Detection Using Unsupervised and Semi-Supervised Technique on High Dimensional Data
... Outlier detection is useful for detection credit card ...Fraud detection refers to the act of finding frauds as early as ...fraud detection has been implemented using techniques such as ... See full document
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MINING OF OUTLIER DETECTION IN LARGE CATEGORICAL DATASETS
... of outlier detection techniques square measure mentioned ...anomaly detection approach learns classifier victimisation tagged objects happiness to the traditional and anomaly categories, and assigns ... See full document
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Enhanced Outlier Detection Method using Association Rule Mining Technique
... of outlier detection, where the aim is to identify data which behave in an unexpected way or have abnormal ...identifies outlier transaction by enhancing an association classification approach ... See full document
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Outlier Detection in Big Data
... Big Data and business analytics for a wide range of tasks including fraud detection (Phua et ...Recent research for financial fraud detec- tion has examined using multi-layered approaches and ... See full document
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Outlier Detection for High Dimensional Data Using Graph Based Models
... aberrant data that may otherwise adversely lead to model misspecification, biased parameter estimation and incorrect ...the data is sparse and the notion of proximity fails to retain its meaningfulness ... See full document
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Intelligent Anomaly Detection Techniques for Denial of Service Attacks
... graduate research assistant in School of Electrical and Computer Engineering at Georgia Institute of Technology, Atlanta GA USA and received the PhD degree under the supervision of ...bioinformatics ... See full document
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An Improvement in Outlier Detection Using Spectral Clustering Algorithm for Data Mining
... techniques. Outlier detection can be done using uni variety as well as multivariate data in terms of categorical as well as continuous ...univariate data, description such as shape, center, ... See full document
6
Privacy Feedback System Using Data Mining and Outlier Detection Algorithm
... confidential data and provides only the necessary information to the people who require to access the ...personal data about the patient like name, age, gender, address and phone ...the research ... See full document
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Outlier Detection on Incremental Data set Using Cell-Based approach in Data Mining
... Squad choice choices and honor selection are some imperative procedures which require much give a second thought and top to bottom examination. Universal Cricket Council (ICC) and related bodies every year hold ... See full document
6
A Heart Disease Prediction Model using Association Rule Mining Technique
... a research article ...disease data is accepted as ...filtered data to get association rules. To detect outliners the technique of linear regression is also implemented to keep fit solution ... See full document
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Medical Insurance Fraud Recognition Based on Improved Outlier Detection Algorithm
... apply data mining to health insurance fraud. IBM Research Center Marisa et al [1] first proposed to use data mining method to detect health insurance fraud, and the use of association ... See full document
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Different Outlier Detection Algorithms in Data Mining: A Review
... Abstract— Outlier is defined as an observation that deviates too much from other ...knowledge. Outlier detection has been extensively studied in the past ...existing research focuses on the ... See full document
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A STUDY ON DIFFERENT APPROACHES OF OUTLIER DETECTION IN DATA MINING
... Various outlier detection methods are proposed by great number of ...of outlier detection & linkage between data mining method and statistical outlier detection ... See full document
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