Clustering structure analysis in time-series data with density-based clusterability measure

Jokinen, Juho; Räty, Tomi; Lintonen, Timo

doi:10.1109/jas.2019.1911744

Cited by 27 publications

(20 citation statements)

References 49 publications

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“…As the distance metric for the K-means, we use the Mahalanobis distance; this distance has properties such as being invariant to scale by nonsingular linear transformations. An in-depth study of different metrics [36,37] will be a specific job to investigate whether it can improve the performance of the proposed methodology. (6) Label each pattern according to the group number in which each pattern (instance) was classified.…”

Section: Complexitymentioning

confidence: 99%

A Methodology to Determine the Subset of Heuristics for Hyperheuristics through Metalearning for Solving Graph Coloring and Capacitated Vehicle Routing Problems

et al. 2021

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In this work, we focus on the problem of selecting low-level heuristics in a hyperheuristic approach with offline learning, for the solution of instances of different problem domains. The objective is to improve the performance of the offline hyperheuristic approach, identifying equivalence classes in a set of instances of different problems and selecting the best performing heuristics in each of them. A methodology is proposed as the first step of a set of instances of all problems, and the generic characteristics of each instance and the performance of the heuristics in each one of them are considered to define the vectors of characteristics and make a grouping of classes. Metalearning with statistical tests is used to select the heuristics for each class. Finally, we used the Naive Bayes to test the set instances with k-fold cross-validation, and we compared all results statistically with the best-known values. In this research, the methodology was tested by applying it to the problems of capacitated vehicle routing (CVRP) and graph coloring (GCP). The experimental results show that the proposed methodology can improve the performance of the offline hyperheuristic approach, correctly identifying the classes of instances and applying the appropriate heuristics in each case. This is based on the statistical comparison of the results obtained with those of the state of the art of each instance.

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Section: Complexitymentioning

confidence: 99%

A Methodology to Determine the Subset of Heuristics for Hyperheuristics through Metalearning for Solving Graph Coloring and Capacitated Vehicle Routing Problems

et al. 2021

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“…Time-series data clustering algorithms (TSDCAs) cluster time-series data by minimizing the dissimilarity of time-series samples in the same cluster while maximizing the dissimilarity of different clusters [45][46][47]. Since the FCM, a TSDCA, has been shown to be effective in time-series data clusters [48][49][50], we employ the FCM to subclassify land cover classes.…”

Section: Class and Subclass Definitionmentioning

confidence: 99%

A Robust Method for Generating High-Spatiotemporal-Resolution Surface Reflectance by Fusing MODIS and Landsat Data

et al. 2020

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The methods for accurately fusing medium- and high-spatial-resolution satellite reflectance are vital for monitoring vegetation biomass, agricultural irrigation, ecological processes and climate change. However, the currently existing fusion methods cannot accurately capture the temporal variation in reflectance for heterogeneous landscapes. In this study, we proposed a new method, the spatial and temporal reflectance fusion method based on the unmixing theory and a fuzzy C-clustering model (FCMSTRFM), to generate Landsat-like time-series surface reflectance. Unlike other data fusion models, the FCMSTRFM improved the similarity of pixels grouped together by combining land cover maps and time-series data cluster algorithms to define endmembers. The proposed method was tested over a 2000 km2 study area in Heilongjiang Provence, China, in 2017 and 2018 using ten images. The results show that the accuracy of the FCMSTRFM is better than that of the popular enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) (correlation coefficient (R): 0.8413 vs. 0.7589; root mean square error (RMSE): 0.0267 vs. 0.0401) and the spatial-temporal data fusion approach (STDFA) (R: 0.8413 vs. 0.7666; RMSE: 0.0267 vs. 0.0307). Importantly, the FCMSTRFM was able to maintain the details of temporal variations in complicated landscapes. The proposed method provides an alternative method to monitor the dynamics of land surface variables over complicated heterogeneous regions.

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“…Equation (7) indicates that cosine similarity distance δ i can be obtained by calculating the minimum distance from the data point x i to any point with a density greater than that. After calculating the two parameters, a decision graph with ρ as the horizontal axis and δ as the vertical axis can be constructed.…”

Section: Definition 2 Local Density ρ I Based On Cosine Similarity (Gaussian Kernel)mentioning

confidence: 99%

“…Consequently, the conventional clustering method cannot process the data accurately. Some research [4][5][6][7][8][9] has proposed improvements on the text clustering algorithm, and some studies [10,11] have proposed improvements on the K-means algorithm. To apply the K-means, it is necessary to specify the number of clusters in advance and randomly select the initial clustering centers.…”

Section: Introductionmentioning

confidence: 99%

High-Dimensional Text Clustering by Dimensionality Reduction and Improved Density Peak

Sun

Platoš

2020

Wireless Communications and Mobile Computing

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This study focuses on high-dimensional text data clustering, given the inability of K-means to process high-dimensional data and the need to specify the number of clusters and randomly select the initial centers. We propose a Stacked-Random Projection dimensionality reduction framework and an enhanced K-means algorithm DPC-K-means based on the improved density peaks algorithm. The improved density peaks algorithm determines the number of clusters and the initial clustering centers of K-means. Our proposed algorithm is validated using seven text datasets. Experimental results show that this algorithm is suitable for clustering of text data by correcting the defects of K-means.

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Clustering structure analysis in time-series data with density-based clusterability measure

Cited by 27 publications

References 49 publications

A Methodology to Determine the Subset of Heuristics for Hyperheuristics through Metalearning for Solving Graph Coloring and Capacitated Vehicle Routing Problems

A Methodology to Determine the Subset of Heuristics for Hyperheuristics through Metalearning for Solving Graph Coloring and Capacitated Vehicle Routing Problems

A Robust Method for Generating High-Spatiotemporal-Resolution Surface Reflectance by Fusing MODIS and Landsat Data

High-Dimensional Text Clustering by Dimensionality Reduction and Improved Density Peak

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