期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2017
卷号：5
期号：3
页码：5786
DOI：10.15680/IJIRCCE.2017.0503256
出版社：S&S Publications
摘要：Clustering time series data is a trouble that has applications in an extensive variety of areas and hasrecently evoked a large amount of research. Time series data may contain large and outliers. In addition, time seriesdata is a one kind of special data set where attributes have a temporal ordering. Therefore clustering of time series datais a good issue in the data mining process. Different techniques and various clustering algorithms have been proposedto assist clustering of time series data sets also different kinds of non-developmentary optimization techniques andclustering algorithms have been applied for clustering multivariate time series data in some applications, usually theyproduces poor efficient results due to the dependency on the initial set of values and their poor performance inmanipulating multiple objectives. Sometimes Time series data doesn’t contain same length and they usually havemissing values, the basic measure Euclidean distance and dynamic time warping cannot be applied for such datasets tomeasure the similarity of objects. The clustering algorithms and its effectiveness on various applications are comparedto develop a new method to solve the existing problem. Henceforth, this presents a survey on dissimilar clusteringalgorithms available for time series datasets. Moreover, the uniqueness and ceiling of previous researches are alsodiscussed and further it contains short descriptive taxonomy of Data Mining Techniques and Tools.
关键词：Clustering techniques; Time Series Data; Pitfalls; Secular Trend; ML Algorithms;Tools
