Density Clustering Framework in Unsupervised Data : Fuzzy Logic Based Sparse Coding Outlier Detection
Language: English
Published by LAP LAMBERT Academic Publishing, 2020
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
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Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - 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.…
Seller Inventory # 9786203040135
Bibliographic details
- Title
- Density Clustering Framework in Unsupervised Data : Fuzzy Logic Based Sparse Coding Outlier Detection
- Author
- A. Subhasheni
- Publisher
- LAP LAMBERT Academic Publishing
- Publication year
- 2020
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 6203040134
- ISBN 13
- 9786203040135
- Item weight
- 143 grams
- Dimensions
- 220x150x6 mm
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