Clustering is a solution for classifying enormous data when there is not any early knowledge
about classes. With emerging new concepts like cloud computing and big data and their vast …

Time series are recorded values of an interesting phenomenon such as stock prices,
household incomes, or patient heart rates over a period of time. Time series data mining …

The problem of similarity search in large time series databases has attracted much attention
recently. It is a non-trivial problem because of the inherent high dimensionality of the data …

In the last decade there has been an explosion of interest in mining time series data.
Literally hundreds of papers have introduced new algorithms to index, classify, cluster and …

With the growing importance of time series clustering research, particularly for similarity
searches amongst long time series such as those arising in medicine or finance, it is critical …

Much of the world's supply of data is in the form of time series. In the last decade, there has
been an explosion of interest in Mining time series data. A nunber of new algorithms have …

Recently there has been significant development in the use of wavelet methods in various
data mining processes. However, there has been written no comprehensive survey …

Functional data sets appear in many areas of science. Although each data point may be
seen as a large finite-dimensional vector it is preferable to think of them as functions, and …

Efficiently and accurately searching for similarities among time series and discovering
interesting patterns is an important and non-trivial problem. In this paper, we introduce a …

We propose a dimensionality reduction technique for time series analysis that significantly
improves the efficiency and accuracy of similarity searches. In contrast to piecewise constant …