scholarly journals An Adaptive Density-Based Time Series Clustering Algorithm: A Case Study on Rainfall Patterns

2016 ◽  
Vol 5 (11) ◽  
pp. 205 ◽  
Author(s):  
Xiaomi Wang ◽  
Yaolin Liu ◽  
Yiyun Chen ◽  
Yi Liu
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Time Series Clustering ◽  
Rainfall Patterns

Clustering Methodology for Time Series Mining

2009 ◽  
Vol 40 (1) ◽  
pp. 81-86
Author(s):  
Pēteris Grabusts ◽  
Arkady Borisov
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Similarity Measures ◽  
Longest Common Subsequence ◽  
Series Data ◽  
Time Series Clustering ◽  
Series Analysis ◽  
Time Series Mining

Clustering Methodology for Time Series MiningA time series is a sequence of real data, representing the measurements of a real variable at time intervals. Time series analysis is a sufficiently well-known task; however, in recent years research has been carried out with the purpose to try to use clustering for the intentions of time series analysis. The main motivation for representing a time series in the form of clusters is to better represent the main characteristics of the data. The central goal of the present research paper was to investigate clustering methodology for time series data mining, to explore the facilities of time series similarity measures and to use them in the analysis of time series clustering results. More complicated similarity measures include Longest Common Subsequence method (LCSS). In this paper, two tasks have been completed. The first task was to define time series similarity measures. It has been established that LCSS method gives better results in the detection of time series similarity than the Euclidean distance. The second task was to explore the facilities of the classical k-means clustering algorithm in time series clustering. As a result of the experiment a conclusion has been drawn that the results of time series clustering with the help of k-means algorithm correspond to the results obtained with LCSS method, thus the clustering results of the specific time series are adequate.

scholarly journals Space–time series clustering: Algorithms, taxonomy, and case study on urban smart cities

2020 ◽  
Vol 95 ◽  
pp. 103857
Author(s):  
Asma Belhadi ◽  
Youcef Djenouri ◽  
Kjetil Nørvåg ◽  
Heri Ramampiaro ◽  
Florent Masseglia ◽  
...  
Keyword(s):  
Time Series ◽  
Smart Cities ◽  
Clustering Algorithms ◽  
Space Time ◽  
Time Series Clustering

scholarly journals Time Series Clustering of Online Gambling Activities for Addicted Users’ Detection

2021 ◽  
Vol 11 (5) ◽  
pp. 2397
Author(s):  
Fernando Peres ◽  
Enrico Fallacara ◽  
Luca Manzoni ◽  
Mauro Castelli ◽  
Aleš Popovič ◽  
...  
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Online Gambling ◽  
Social Experiment ◽  
Time Series Clustering ◽  
Pathological Gamblers ◽  
Beneficial Effects ◽  
Personal Consequences ◽  
Online Gamblers ◽  
Gambling Activities

Ever since the worldwide demand for gambling services started to spread, its expansion has continued steadily. To wit, online gambling is a major industry in every European country, generating billions of Euros in revenue for commercial actors and governments alike. Despite such evidently beneficial effects, online gambling is ultimately a vast social experiment with potentially disastrous social and personal consequences that could result in an overall deterioration of social and familial relationships. Despite the relevance of this problem in society, there is a lack of tools for characterizing the behavior of online gamblers based on the data that are collected daily by betting platforms. This paper uses a time series clustering algorithm that can help decision-makers in identifying behaviors associated with potential pathological gamblers. In particular, experimental results obtained by analyzing sports event bets and black jack data demonstrate the suitability of the proposed method in detecting critical (i.e., pathological) players. This algorithm is the first component of a system developed in collaboration with the Portuguese authority for the control of betting activities.

scholarly journals Linear Time Complexity Time Series Clustering with Symbolic Pattern Forest

2019 ◽  
Author(s):  
Xiaosheng Li ◽  
Jessica Lin ◽  
Liang Zhao
Keyword(s):  
Time Series ◽  
Data Storage ◽  
Time Complexity ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Linear Time ◽  
Series Data ◽  
Data Generation ◽  
Time Series Clustering ◽  
Group Structures

With increasing powering of data storage and advances in data generation and collection technologies, large volumes of time series data become available and the content is changing rapidly. This requires the data mining methods to have low time complexity to handle the huge and fast-changing data. This paper presents a novel time series clustering algorithm that has linear time complexity. The proposed algorithm partitions the data by checking some randomly selected symbolic patterns in the time series. Theoretical analysis is provided to show that group structures in the data can be revealed from this process. We evaluate the proposed algorithm extensively on all 85 datasets from the well-known UCR time series archive, and compare with the state-of-the-art approaches with statistical analysis. The results show that the proposed method is faster, and achieves better accuracy compared with other rival methods.

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