Outlier Detection using Soft Computing Techniques
Detecting Deviant Objects in Various Information Systems using Soft Computing Methods
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With the growth of the digital era, data is largely available, so knowledge retrieval from those data is done by data mining algorithms. Among various data mining algorithms, finding outliers is crucial as their occurrence degrades system efficiency. The majority of the research was limited to detecting outliers in a single universe with a single granulation for numerical or categorical data. The existing machine learning outlier detection algorithms work well for quantitative data but they are not directly applied to qualitative, vague and imprecise data which produces ineffective results. Th...
With the growth of the digital era, data is largely available, so knowledge retrieval from those data is done by data mining algorithms. Among various data mining algorithms, finding outliers is crucial as their occurrence degrades system efficiency. The majority of the research was limited to detecting outliers in a single universe with a single granulation for numerical or categorical data. The existing machine learning outlier detection algorithms work well for quantitative data but they are not directly applied to qualitative, vague and imprecise data which produces ineffective results. There is also ambiguous, uncertain, incomplete, and indeterminate information that persists in this real world. These problems are handled in this research work using rough set theory, intuitionistic fuzzy, and neutrosophic sets. The proposed methodology rough entropy based weighted density outlier detection method has been designed to detect outliers for various information systems. The weighted density value for each object and attribute has been determined to detect outliers. So a true object will never be treated as an outlier.
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