Subsequence-Based Time Series Clustering Utilizing Stochastic Selection Methods

We first introduce related works on clustering for time series and constrained clustering, respectively in section 2.1 and 2.2 , then we present the deep constrained

Hierarchical clustering and network analysis methods are used to perform cluster analysis on stock price time series of 594 US- based companies in order to verify whether stock

In this paper presents an enhanced method such as Similarity Measure Selection for Clustering Time Series Databases using Agglomerative Hierarchical algorithm

Both measures are discussed, a model for clustering discrete time series is introduced and the applicability of the new algorithm is demonstrated on a quite large data set from

Here we give a forecasting method based on F-transforms for seasonal time series

Chonghui GUO [22] presented a novel feature based approach to time series clustering which first converted the raw time series into feature vector of lower dimension

A er the fi rst experiment (Fejfar, Weinlichová, Šťastný, 2009) it seems that the whole concept of the clustering algorithm is correct but that we have to perform time series

The thesis is organized into 6 chapters. In Chapter 2, related density based clustering approaches are discussed along with their shortcomings on handling certain