Increasing global building energy demand, with the related economic and environmental
impact, upsurges the need for the design of reliable energy demand forecast models. This …

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 …

This paper presents a clustering-based strategy to identify typical daily electricity usage
(TDEU) profiles of multiple buildings. Different from the majority of existing clustering …

This paper introduces a two-stage deep learning-based methodology for clustering time
series data. First, a novel technique is introduced to utilize the characteristics (eg, volatility) …

Clustering of subsequence time series remains an open issue in time series clustering.
Subsequence time series clustering is used in different fields, such as e‐commerce, outlier …

Mobile network traffic is increasing in an unprecedented manner, resulting in growing
demand from network operators to deploy more base stations able to serve more devices …

Massive bike-sharing systems (BSS) usage and performance data have been collected for
years over various locations. Nevertheless, researchers encountered several challenges …

To analyze time series data with large volume, most of the existing clustering algorithms
focus on data reduction techniques or multi-level strategies. However, the destruction of raw …

The existing body of research on dynamic customer segmentation has primarily focused on
segment-level customer purchasing behavior (CPB) analysis to tailor marketing strategies …

Abstract The Longest Common Subsequence (LCSS) is considered as a classic problem in
computer science. In most studies related to time series data mining, LCSS had been …