Research On Large outliers in the data set data mining algorithm

DOI

10.2991/icmmct-16.2016.346How to use a DOI?

Keywords

Data mining, outlier detection, outlier analysis, clustering, classification

Abstract

Main purpose of outliers mining is from a large number of, incomplete, there are all kinds of data, the found hidden in one of the people is not known in advance but potentially valuable information or knowledge. Outlier is a data: deviate significantly from other data, it does not meet the general patterns or behavior. Outlier data mining has been widely used in the stock market, telecommunications, financial services, intrusion detection, weather forecasting and many other fields. Outliers may be "noise", but it may also be significant events. In practice, in some applications, those rare events are likely to have more value than events that occur frequently. Unfortunately, the outlier data mining is a very important and meaningful work.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jinhai Zhang
PY  - 2016/03
DA  - 2016/03
TI  - Research On Large outliers in the data set data mining algorithm
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology
PB  - Atlantis Press
SP  - 1742
EP  - 1746
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmct-16.2016.346
DO  - 10.2991/icmmct-16.2016.346
ID  - Zhang2016/03
ER  -