Published Paper Details:

Time series data are observations collected sequentially over time. Consider as example weather prediction. When observations are collected at frequent time intervals, large data sets in the form of time series are generated. Outlier detection is a primary step in many data mining. An outlier is a piece of data or observation that deviates from other observations, outliers is not noise. An outlier may be due to variability in the measurement. To identify & remove outliers is challenging task in data mining. There are many algorithms for outlier detection. There are various algorithms for outlier detection are proposed earlier, some of them are k-mean, Density based, EM etc. They are proposed to detect the outliers. In these algorithm the time is an important attribute of each dataset, and also it is important in the process of data mining for giving the more accurate and useful information. In this work, algorithms are analysing by using WEKA tool. In our research work, the algorithms analysed are K-Mean, Density based, EM, Cobweb.