A Comprehensive Survey of Clustering Algorithms

Xu, Daolin; Tian, Yingjie

doi:10.1007/s40745-015-0040-1

Cited by 1,339 publications

(726 citation statements)

References 124 publications

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“…Evaluation indicators may be subdivided into two categories: internal evaluation and external evaluation. The first one refers to data within the same cluster, while the second one refers to similarity evaluation among data lying in different clusters [53]. Some of the most widely adopted internal evaluation methods are:…”

Section: Discussionmentioning

confidence: 99%

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Explorative Multidimensional Analysis for Energy Efficiency: DataViz versus Clustering Algorithms

Cottafava

Sonetti²,

Gambino

et al. 2018

Energies

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Abstract:We propose a simple tool to help the energy management of a large building stock defining clusters of buildings with the same function, setting alert thresholds for each cluster, and easily recognizing outliers. The objective is to enable a building management system to be used for detection of abnormal energy use. We start reviewing energy performance indicators, and how they feed into data visualization (DataViz) tools for a large building stock, especially for university campuses. After a brief presentation of the University of Turin's building stock which represents our case study, we perform an explorative analysis based on the Multidimensional Detective approach by Inselberg, using the Scatter Plot Matrix and the Parallel Coordinates methods. The k-means clustering algorithm is then applied on the same dataset to test the hypotheses made during the explorative analysis. Our results show that DataViz techniques provide quick and user-friendly solutions for the energy management of a large stock of buildings. In particular, they help identifying clusters of buildings and outliers and setting alert thresholds for various Energy Efficiency Indices.

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Section: Discussionmentioning

confidence: 99%

“…Finally, there are various external evaluation indices, as reported by Dongkuan et al [53] (i.e., Rand index [57], Jaccard index [58], Fowlkes-Mallows index [59], etc. ), useful to evaluate the efficiency of clustering algorithms in terms of finding true (false) positives and negatives with respect to a reference cluster configuration.…”

Section: Discussionmentioning

confidence: 99%

Explorative Multidimensional Analysis for Energy Efficiency: DataViz versus Clustering Algorithms

Cottafava

Sonetti²,

Gambino

et al. 2018

Energies

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show abstract

“…In fact, no particular clustering method has been shown to be superior to its competitors with regards to all of the necessary aspects [42]. To date, the advantages and disadvantages of various algorithms have been extensively analyzed [43][44][45][46]. Clustering attributes are the most important judgment criteria in clustering calculations.…”

Section: Layout Optimization Of Refueling Service Based On Cluster Anmentioning

confidence: 99%

A Distance-Adaptive Refueling Recommendation Algorithm for Self-Driving Travel

Yang

Peng

et al. 2018

IJGI

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Abstract:Taking the maximum vehicle driving distance, the distances from gas stations, the route length, and the number of refueling gas stations as the decision conditions, recommendation rules and an early refueling service warning mechanism for gas stations along a self-driving travel route were constructed by using the algorithm presented in this research, based on the spatial clustering characteristics of gas stations and the urgency of refueling. Meanwhile, by combining ArcEngine and Matlab capabilities, a scenario simulation system of refueling for self-driving travel was developed by using c#.net in order to validate and test the accuracy and applicability of the algorithm. A total of nine testing schemes with four simulation scenarios were designed and executed using this algorithm, and all of the simulation results were consistent with expectations. The refueling recommendation algorithm proposed in this study can automatically adapt to changes in the route length of self-driving travel, the maximum driving distance of the vehicle, and the distance from gas stations, which could provide variable refueling recommendation strategies according to differing gas station layouts along the route. Therefore, the results of this study could provide a scientific reference for the reasonable planning and timely supply of vehicle refueling during self-driving travel.

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“…In fact, no particular clustering method has been shown to be superior to its competitors with regards to all the necessary aspects [25,26]. To date, the advantages and disadvantages of various algorithms have been extensively analyzed [26][27][28][29][30][31].An analysis of the classical spatial clustering algorithms is shown in Table 1.…”

Section: Of 23mentioning

confidence: 99%

An Adaptive Sweep-Circle Spatial Clustering Algorithm Based on Gestalt

Zhan¹,

Deng²,

Zheng³

2017

Preprint

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An adaptive spatial clustering (ASC) algorithm is proposed in this present study, which employs sweep-circle techniques and a dynamic threshold setting based on the Gestalt theory to detect spatial clusters. The proposed algorithm can automatically discover clusters in one pass, rather than through the modification of the initial model (for example, a minimal spanning tree, Delaunay triangulation or Voronoi diagram). It can quickly identify arbitrarily-shaped clusters while adapting efficiently to non-homogeneous density characteristics of spatial data, without the need of prior knowledge or parameters. The proposed algorithm is also ideal for use in data streaming technology with dynamic characteristics flowing in the form of spatial clustering in large data sets.

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A Comprehensive Survey of Clustering Algorithms

Cited by 1,339 publications

References 124 publications

Explorative Multidimensional Analysis for Energy Efficiency: DataViz versus Clustering Algorithms

Explorative Multidimensional Analysis for Energy Efficiency: DataViz versus Clustering Algorithms

A Distance-Adaptive Refueling Recommendation Algorithm for Self-Driving Travel

An Adaptive Sweep-Circle Spatial Clustering Algorithm Based on Gestalt

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