Outlier Detection of Energy House Holding Data Streams using K-Means and A-SVM Algorithm

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Volume 5, Issue 5 (May, 2019)
Publication No:
Maninder Kaur, Mandeep Singh Saini
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Outlier detection or anomaly detection is an important and challenging issue in data mining, even so in the domain of energy data mining where data are often collected in large amounts but with little labeled information. There is a need for pre-processing of the raw data in many fields, such as data mining, information retrieval, machine learning and pattern recognition. Data Mining or Knowledge discovery refers to a variety of techniques that have developed in the fields of databases, machine learning and pattern recognition. Data pre-processing involves many tasks including detecting outliers, recovering incomplete data and correcting errors. Outlier detection is an important pre-processing task. Outlier detection is a task that finds objects that are dissimilar or inconsistent with respect to the remaining data. Outlier detection can be done using clustering methods. In this paper, an efficient outlier detection method has been proposed which is based on Fuzzy clustering using Artificial Bee Colony algorithm. The K-PCA and K-means clustering based on Artificial Bee Colony algorithm is performed, and small clusters are calculated and considered as outlier clusters. K-means clustering is used to choose the cluster heads and ABC –SVM to select the members of the clusters or classes. Test result shows the effective results in finding the outliers on data sets in data mining literature.

Outlier Detection, K-PCA, ABC –SVM and Vector, Data Mining and K-means Clustering and Values.