| Type of Material: | Thesis |
| Title: | Characterising selected algorithms used for outlier detection and developing improved combinations |
| Researcher: | Divya, D |
| Guide: | Bhasi, M and Santosh Kumar, M B |
| Department: | Department of Information Technology |
| Publisher: | Cochin University of Science & Technology, Cochin |
| Place: | Cochin |
| Year: | 2022 |
| Language: | English |
| Subject: | Artificial Neural Networks | Computer Science Interdisciplinary Applications | Data Distribution | Engineering and Technology | Machine Learning | Computer Science and Information Technology | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Information Technology, Cochin University of Science & Technology, Cochin, Cochin; 2022 |
| Fulltext: | Shodhganga |
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| 001 | 456402 | |
| 003 | IN-AhILN | |
| 005 | 2024-10-09 16:29:37 | |
| 008 | __ | 241009t2022||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_456402 |
| 040 | __ | |aCUST_682022|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aDivya, D|eResearcher |
| 110 | __ | |aDepartment of Information Technology|bCochin University of Science & Technology, Cochin|dCochin|ein|0U-0253 |
| 245 | __ | |aCharacterising selected algorithms used for outlier detection and developing improved combinations |
| 260 | __ | |aCochin|bCochin University of Science & Technology, Cochin|c2022 |
| 300 | __ | |axii,306|dDVD |
| 502 | __ | |cDepartment of Information Technology, Cochin University of Science & Technology, Cochin, Cochin|d2022|bPhD |
| 518 | __ | |d2023|oDate of Award |
| 518 | __ | |oDate of Registration|d2017 |
| 520 | __ | |aData analysis is becoming very important, and outlier detection is one major type of data analysis problem. Outliers are points that are different from the remaining dataset. Outliers contain useful information regarding abnormal characteristics of the systems and entities that impact the data generation process. Recognition of such abnormal characteristics can lead to useful application-specific insights, especially in intrusion detection and fraud detection cases. Although there are many outlier identification approaches, they do not all perform equally well on all types of datasets. Therefore, it can be assumed that certain tools and techniques of outlier detection will perform well with datasets having certain characteristics only. The main objective of this work is to explore and find a match between the dataset characteristics and tools or techniques of outlier detection that perform well with a given dataset type. Algorithm developers are interested in creating new outlier detection algorithms. Algor |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aArtificial Neural Networks |
| 653 | __ | |aComputer Science Interdisciplinary Applications |
| 653 | __ | |aData Distribution |
| 653 | __ | |aEngineering and Technology |
| 653 | __ | |aMachine Learning |
| 700 | __ | |eGuide|aBhasi, M and Santosh Kumar, M B |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/512664|yShodhganga |
| 905 | __ | |afromsg |
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