DLCSS: A new similarity measure for time series data mining

Soleimani, Gholamreza; Abessi, Masoud

doi:10.1016/j.engappai.2020.103664

Cited by 11 publications

(4 citation statements)

References 20 publications

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“…Although the time complexity was reduced by 50 per cent, the accuracy was also reduced (Rahim Khan and Zakarya, 2013). Subsequently, Soleimani and Abessi improved the accuracy of the clustering by changing this measure to a fuzzy approach; however, the time complexity also increased (Soleimani and Abessi, 2020). Kamalzadeh et al presented a new distance measure for long-time clustering series using geometric relations (Kamalzadeh et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

“…In previous studies, two different approaches have been studied, including definition and improvement or combination of distance measures (Kamalzadeh et al , 2020; Soleimani and Abessi, 2020; Łuczak, 2016). At first, it was endeavored to improve the clustering by defining a new distance measure or improving preexisting distance measures.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

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A new method based on ensemble time series for fast and accurate clustering

Ghorbanian

Razavi²

2023

DTA

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PurposeThe common methods for clustering time series are the use of specific distance criteria or the use of standard clustering algorithms. Ensemble clustering is one of the common techniques used in data mining to increase the accuracy of clustering. In this study, based on segmentation, selecting the best segments, and using ensemble clustering for selected segments, a multistep approach has been developed for the whole clustering of time series data.Design/methodology/approachFirst, this approach divides the time series dataset into equal segments. In the next step, using one or more internal clustering criteria, the best segments are selected, and then the selected segments are combined for final clustering. By using a loop and how to select the best segments for the final clustering (using one criterion or several criteria simultaneously), two algorithms have been developed in different settings. A logarithmic relationship limits the number of segments created in the loop.FindingAccording to Rand's external criteria and statistical tests, at first, the best setting of the two developed algorithms has been selected. Then this setting has been compared to different algorithms in the literature on clustering accuracy and execution time. The obtained results indicate more accuracy and less execution time for the proposed approach.Originality/valueThis paper proposed a fast and accurate approach for time series clustering in three main steps. This is the first work that uses a combination of segmentation and ensemble clustering. More accuracy and less execution time are the remarkable achievements of this study.

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Section: Related Workmentioning

confidence: 99%

Section: Introduction and Literature Reviewmentioning

confidence: 99%

A new method based on ensemble time series for fast and accurate clustering

Ghorbanian

Razavi²

2023

DTA

View full text Add to dashboard Cite

show abstract

“…The default distance measure is the Euclidean distance. There are many alternative elastic distance measures in the literature that can give better results, the most common being dynamic time warping (DTW) [ 15 ], longest common subsequence (LCS) [ 16 ], edit distance with real penalty (ERP), and edit distance on real sequence (EDR) [ 17 ]. They are able to shrink or stretch the time axis to find the best alignment between the time series and obtain the smallest distance between them [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Temporal Convolution Network for Time Series Classification

Koh

Lim

Rahimi

et al. 2021

Sensors

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A neural network that matches with a complex data function is likely to boost the classification performance as it is able to learn the useful aspect of the highly varying data. In this work, the temporal context of the time series data is chosen as the useful aspect of the data that is passed through the network for learning. By exploiting the compositional locality of the time series data at each level of the network, shift-invariant features can be extracted layer by layer at different time scales. The temporal context is made available to the deeper layers of the network by a set of data processing operations based on the concatenation operation. A matching learning algorithm for the revised network is described in this paper. It uses gradient routing in the backpropagation path. The framework as proposed in this work attains better generalization without overfitting the network to the data, as the weights can be pretrained appropriately. It can be used end-to-end with multivariate time series data in their raw form, without the need for manual feature crafting or data transformation. Data experiments with electroencephalogram signals and human activity signals show that with the right amount of concatenation in the deeper layers of the proposed network, it can improve the performance in signal classification.

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“…Data mining professionals tend to use fast, unsupervised methods in the early stages of the data mining process. Thus, data mining tasks (e.g., motif discovery, discord discovery, clustering, and segmentation) should be handled for time series data [4][5][6][7][8]. However, time series have anomalies due to similarities [9,10].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Algorithm for Anomaly Removal in Time Series Data Mining

Razaque¹,

Abenova²,

Alotaibi³

et al. 2021

Preprint

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Time series data are significant and are derived from temporal data, which involve real numbers representing values collected regularly over time. Time series have a great impact on many types of data. However, time series have anomalies. We introduce hybrid algorithm named novel matrix profile (NMP) to solve the all-pairs similarity search problem for time series data. The proposed NMP inherits the features from two state-of-the art algorithms: similarity time-series automatic multivariate prediction (STAMP), and short text online microblogging protocol (STOMP). The proposed algorithm caches the output in an easy-to-access fashion for single- and multidimensional data. The proposed NMP algorithm can be used on large data sets and generates approximate solutions of high quality in a reasonable time. The proposed NMP can also handle several data mining tasks. It is implemented on a Python platform. To determine its effectiveness, it is compared with the state-of-the-art matrix profile algorithms i.e., STAMP and STOMP. The results confirm that the proposed NMP provides higher accuracy than the compared algorithms.

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DLCSS: A new similarity measure for time series data mining

Cited by 11 publications

References 20 publications

A new method based on ensemble time series for fast and accurate clustering

A new method based on ensemble time series for fast and accurate clustering

Deep Temporal Convolution Network for Time Series Classification

Hybrid Algorithm for Anomaly Removal in Time Series Data Mining

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