Active and Dynamic Approaches for Clustering Time Dependent Information: Lag Target Time Series Clustering and Multi-Factor Time Series Clustering

Time-Series Data Clustering

15.

Subsequence Time Series Clustering

INTRODUCTION Clustering analysis is a tool used widely in the Data Mining community and beyond (Everitt et al. 2001). In essence, the method allows us to " summarise " the information in a large data set X by creating a very much smaller set C of representative points (called centroids) and a membership map relating each point in X to its representative in C. An obvious but special type of data...

Spectral Clustering for Time Series

This paper presents a general framework for time series clustering based on spectral decomposition of the affinity matrix. We use the Gaussian function to construct the affinity matrix and develop a gradient based method for self-tuning the variance of the Gaussian function. The feasibility of our method is guaranteed by the theoretical inference in this paper. And our approach can be used to c...

A Hybrid Time Series Clustering Method Based on Fuzzy C-Means Algorithm: An Agreement Based Clustering Approach

In recent years, the advancement of information gathering technologies such as GPS and GSM networks have led to huge complex datasets such as time series and trajectories. As a result it is essential to use appropriate methods to analyze the produced large raw datasets. Extracting useful information from large data sets has always been one of the most important challenges in different sciences,...

Clustering Time Series with Hidden Markov Modelsand Dynamic Time