A Hybrid Algorithm for Clustering of Time Series Data Based on Affinity Search Technique

Aghabozorgi, Saeed; Wah, Teh Ying; Herawan, Tutut; Jalab, Hamid A.; Shaygan, Mohammad Amin; Jalali, Alireza

doi:10.1155/2014/562194

Cited by 49 publications

(35 citation statements)

References 59 publications

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“…Similarity in time means that the similarity between two time series based on similarity at any given moment in time. Similarity in shape is the similarity between two time series based on the similarities between the following subsequence and the similarity in model also means the uniformity of the parameters and the uniformity of the fitted model to two time series [9].…”

Section: Similarity and History Of Similarity Measurement Methodsmentioning

confidence: 99%

A New Similarity Measure for Time Series Data Mining Based on Longest Common Subsequence

Soleimany¹,

Abessi²

2019

AJDMKD

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In this research, a new similarity measurement method that named Developed Longest Common Subsequence (DLCSS) is suggested for time series data mining. The main idea of the DLCSS is using the logic of the Longest Common Subsequence (LCSS) method and the concept of similarity in time series data. In most studies related to time series data mining, referred to the LCSS and Dynamic Time Warping (DTW) methods as the best and most usable for similarity measurement methods, but the LCSS is intrinsically designed to measure the similarity of two sequences of character, which later was developed for time series by defining and determining the similarity threshold. The value of similarity threshold has huge impact on the quality of time series data mining. In the DLCSS by defining two similarity thresholds and determining the values of them, this defect is eliminated. The performance of the DLCSS will be compared with the LCSS and DTW in time series data mining by the Query by content and K-medoids Clustering techniques on 23 datasets from the UCR datasets. The result shows that it is possible to claim that the performance of the DLCSS is better than the LCSS and DTW with 90% confidence.

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Section: Similarity and History Of Similarity Measurement Methodsmentioning

confidence: 99%

A New Similarity Measure for Time Series Data Mining Based on Longest Common Subsequence

Soleimany¹,

Abessi²

2019

AJDMKD

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“…An example is Self-Organizing Maps (SOM), which is a model-based clustering approach based on neural networks [10]. -Multi-step The Multi-step time series clustering refers to a combination of methods (also called a hybrid method), which is employed to improve the quality of cluster representation [11,12].…”

Section: General Time Series Clustering Approachesmentioning

confidence: 99%

An autoencoder-based deep learning approach for clustering time series data

et al. 2020

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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 (e.g., volatility) of the given time series data in order to create labels and thus enable transformation of the problem from an unsupervised into a supervised learning. Second, an autoencoderbased deep learning model is built to model both known and hidden non-linear features of time series data. The paper reports a case study in which the selected financial and stock time series data of over 70 stock indices are clustered into distinct groups using the introduced two-stage procedure. The results show that the proposed methodology is capable of achieving 87.5% accuracy in clustering and predicting the labels for unseen time series data. The paper also reports an important finding in which it is observed that the performance of both techniques (i.e., autoencoder and Kmeans) are comparable. However, there are a few instances of time series data that are classified differently by the autoencoder-based methodology compared to the Kmeans algorithm. The results may indicate that the proposed deep learning-based approach is taking into account additional hidden features that might be overlooked by conventional Kmeans. The finding raises the question whether the explicit features of data should be analyzed for clustering or more advanced techniques such as deep learning need to be adapted by which hidden features and relationships are explored for clustering purposes.

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“…A supervised data is added during the clustering process that further minimizes the errors in the results. Aghabozorgi et al [31] have used time-series data to formulate a unique hybrid clustering algorithm along with k-medoids. The study outcome of the presented work is assessed using accuracy over the cardinality of the datasets.…”

Section: Existing Research Workmentioning

confidence: 99%

Appraising Research Direction & Effectiveness of Existing Clustering Algorithm for Medical Data

Sudha¹,

Girijamma²

2017

ijacsa

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Abstract-The applicability and effectiveness of clustering algorithms had unquestioningly benefitted solving various sectors of real-time problems. However, with the changing time, there is a significant change in forms of the data. This paper briefs about the different taxonomies of the clustering algorithm and highlights the frequently used techniques to understand the research popularity. We also discuss the existing direction of the research work and find that still there is a significant amount of open issues when it comes to clustering medical data. We find that existing techniques are quite symptomatic in nature on local problems in clustering while problems associated with complex medical data are yet to be explored by the researchers. We believe that this manuscript will give a good summary of the effectiveness of existing clustering techniques towards medical data as a contribution.

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A Hybrid Algorithm for Clustering of Time Series Data Based on Affinity Search Technique

Cited by 49 publications

References 59 publications

A New Similarity Measure for Time Series Data Mining Based on Longest Common Subsequence

A New Similarity Measure for Time Series Data Mining Based on Longest Common Subsequence

An autoencoder-based deep learning approach for clustering time series data

Appraising Research Direction & Effectiveness of Existing Clustering Algorithm for Medical Data

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