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 …

In almost every scientific field, measurements are performed over time. These observations
lead to a collection of organized data called time series. The purpose of time-series data …

The proliferation and ubiquity of temporal data across many disciplines has generated
substantial interest in the analysis and mining of time series. Clustering is one of the most …

Computing trajectory similarity is a fundamental operation in movement analytics, required
in search, clustering, and classification of trajectories, for example. Yet the range of different …

Time series is an important class of temporal data objects and it can be easily obtained from
scientific and financial applications. A time series is a collection of observations made …

The previous decade has brought a remarkable increase of the interest in applications that
deal with querying and mining of time series data. Many of the research efforts in this context …

Dynamic time war** (DTW), which finds the minimum path by providing non-linear
alignments between two time series, has been widely used as a distance measure for time …

The last decade has witnessed a tremendous growths of interests in applications that deal
with querying and mining of time series data. Numerous representation methods for …

The proliferation and ubiquity of temporal data across many disciplines has generated
substantial interest in the analysis and mining of time series. Clustering is one of the most …

Time series are ubiquitous, and a measure to assess their similarity is a core part of many
computational systems. In particular, the similarity measure is the most essential ingredient …