Comparative Analysis of Point Outlier Detection using Cluster Based Techniques

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dc.contributor.authorBhatt, Kishor Prasad
dc.date.accessioned2022-04-22T05:27:59Z
dc.date.available2022-04-22T05:27:59Z
dc.date.issued2019
dc.description.abstractOutlier detection is the process of finding peculiar pattern from given set of data. Nowadays,outlier detection is more popular subject in different knowledge domain. Data size is rapidly increases every year there is need to detect outlier in large dataset as early as possible. In this research, comparison of three different cluster based outlier detection algorithm i.e. Kmeans with OD, Partition around medoids (PAM) with OD and density based spatial clustering algorithm with noise (DBSCAN) is presented. The main aim of this research is to evaluate their performance of those three different cluster based outlier algorithm for different dataset with different dimension. The dataset used for this research are chosen such way that they are different in size, mainly in terms of number of instances and attributes. When comparing the performance of all three cluster based outlier detection algorithms, in average PAM with OD is found to bebetter algorithm to detect outlier in most cases with accuracy level 98.30% as well as 92.20% precision, 95.30% recall and 93.43% F-measure value.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14540/9968
dc.language.isoen_USen_US
dc.publisherDepartment of Computer Science & Information Technologyen_US
dc.subjectOutlier detectionen_US
dc.subjectK-means with ODen_US
dc.subjectPAM with ODen_US
dc.subjectDBSCAN (Density based spatial clustering algorithm with noise)en_US
dc.titleComparative Analysis of Point Outlier Detection using Cluster Based Techniquesen_US
dc.typeThesisen_US
local.academic.levelMastersen_US
local.institute.titleCentral Department of Computer Science and Information Technologyen_US
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