SPATIAL OUTLIER DETECTION TECHNIQES

COMPUTER SCIENCE

Outliers can be defined as observations which appear to be inconsistent with the remainder of the dataset. They deviate too much from other observations. Outlier detection is a data mining technique like classification, clustering, and association rules. Recently, a few studies have been conducted on spatial outlier detection for large datasets. This paper focuses on the question how Outlier can be detected. There are many known algorithms for detecting outliers, but most of them are not fast enough when the underlying probability distribution is unknown, the size of the data set is large, and the number of dimensions in the space is high. There are, however, applications that need tools for fast detection of outliers in exactly such situations. Planners are concerned about development; it provides a decision support for development process.

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