Time-Series Data Mining:A Review

Suman H. Pal, Jignasa N. Patet

DOI: http://dx.doi.org/10.5138/bjdmn.v5i1.1645

Abstract


Data mining refers to the extraction of knowledge by analyzing the data from different perspectives and accumulates them to form useful information which could help the decision makers to take appropriate decisions. Classification and clustering has been the two broad areas in data mining. As the classification is a supervised learning approach, the clustering is an unsupervised learning approach and hence can be performed without the supervision of the domain experts. The basic concept is to group the objects in such a way so that the similar objects are closer to each. Time series data is observation of the data over a period of time. The estimation of the parameter, outlier detection and transformation of the data are some ofthe basic issues in handling the time series data. An approach is given for clustering the data based on the membership values assigned to each data point compressing the effect of outlier or noise present in the data. The Possibilistic Fuzzy C-Means (PFCM) with Error Prediction (EP) are done for the clustering and noise identification in the time-series data.

Keywords


Data Mining, Clustering Algorithm,FCM ,PFCM.

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