Outlier detection is the process of finding the data that behave very differently from the normal... more Outlier detection is the process of finding the data that behave very differently from the normal expected behavior. Outliers may be due to system errors, noise or human intended action. Outlier detection becomes an important task in any application to provide reliable and effective results. Many outlier detection methods are proposed amongst SVDD is most commonly used method. This paper provides a brief survey on the outlier detection methods.
Outliers are the data objects that clearly differ in their behavior from the normal data. Outlier... more Outliers are the data objects that clearly differ in their behavior from the normal data. Outlier detection mainly aims at finding these data objects. Outlier detection has become the major area of research in data mining. This plays a crucial role in data mining. Most of the methods used for outlier detection, consider the positive data and their behavior, and then the data violating the behavior are termed as outliers. Most of the time the data may be corrupted making it difficult to identify the data clearly. To handle this problem the paper provides an approach to improve the classification efficiency by generating the likelihood value for each data in the dataset. The kernel K-means clustering is used to compute the likelihood value which defines the membership value towards each class. The data with the value is subjected to classifier thus improving the accuracy in outlier detection.
International Journal of Research in Engineering and Technology, 2016
Outlier detection is the important concept in data mining. These outliers are the data that diffe... more Outlier detection is the important concept in data mining. These outliers are the data that differ from the normal data. Noise in the application may cause the misclassification of data. Data are more likely to be mislabeled in presence of noise leading to performance degradation. The proposed work focuses on these issues. Data before classifying is given a value that represents its willingness towards the class. This data with likelihood value is then given to classifier to predict the data. SVDD algorithm is used for classification of data with likelihood values.
Outlier detection is the process of finding the data that behave very differently from the normal... more Outlier detection is the process of finding the data that behave very differently from the normal expected behavior. Outliers may be due to system errors, noise or human intended action. Outlier detection becomes an important task in any application to provide reliable and effective results. Many outlier detection methods are proposed amongst SVDD is most commonly used method. This paper provides a brief survey on the outlier detection methods.
Outliers are the data objects that clearly differ in their behavior from the normal data. Outlier... more Outliers are the data objects that clearly differ in their behavior from the normal data. Outlier detection mainly aims at finding these data objects. Outlier detection has become the major area of research in data mining. This plays a crucial role in data mining. Most of the methods used for outlier detection, consider the positive data and their behavior, and then the data violating the behavior are termed as outliers. Most of the time the data may be corrupted making it difficult to identify the data clearly. To handle this problem the paper provides an approach to improve the classification efficiency by generating the likelihood value for each data in the dataset. The kernel K-means clustering is used to compute the likelihood value which defines the membership value towards each class. The data with the value is subjected to classifier thus improving the accuracy in outlier detection.
International Journal of Research in Engineering and Technology, 2016
Outlier detection is the important concept in data mining. These outliers are the data that diffe... more Outlier detection is the important concept in data mining. These outliers are the data that differ from the normal data. Noise in the application may cause the misclassification of data. Data are more likely to be mislabeled in presence of noise leading to performance degradation. The proposed work focuses on these issues. Data before classifying is given a value that represents its willingness towards the class. This data with likelihood value is then given to classifier to predict the data. SVDD algorithm is used for classification of data with likelihood values.
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