Density Clustering Framework in Unsupervised Data

Language: English

Published by LAP LAMBERT Academic Publishing Nov 2020, 2020

6203040134 / 9786203040135

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This item is printed on demand - Print on Demand Titel. Neuware -Anomaly detection is a fundamental issue in data mining, specifically it has been used to detect and remove anomalous objects from data. Outliers arise due to mechanical faults, changes in system behavior, fraudulent behavior, network intrusions or human error. An efficient outlier detection and data clustering capabilities in the presence of outliers, and based on filtering of the data after clustering process. The proposed algorithm detects the outlier experiments into three stages namely (i) Saliency Detection in Images; (ii) Abnormal Event Detection in Video Streams; and (iii) Real-world UCI benchmark datasets.The main objective of this study is an iterative removal of objects, which are far away from their cluster centroids. The removal occurs according to a chosen from the pre-defined threshold.The formal framework in which accurate definitions of sparse combinations can be given, and Fuzzy logic is proposed for discover non-linear relationship can be rigorously analyze.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 84 pp. Englisch.…

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Bibliographic details

Title
Density Clustering Framework in Unsupervised Data
Author
A. Subhasheni
Publisher
LAP LAMBERT Academic Publishing Nov 2020
Publication year
2020
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
6203040134
ISBN 13
9786203040135
Item weight
143 grams
Dimensions
220x150x6 mm

buchversandmimpf2000

Emtmannsberg, BAYE, Germany

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