A fuzzy clustering ensemble based on cluster clustering and iterative Fusion of base clusters

Mojarad, Musa; Nejatian, Samad; Parvïn, Hamïd; Mohammadpoor, Majid

doi:10.1007/s10489-018-01397-x

Cited by 60 publications

(30 citation statements)

References 52 publications

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“…(6) Grid-based algorithms [20,21], similar to densitybased algorithms, do clustering on grid merging and segmenting, but they are not suitable for clusters with different densities. (7) Hybrid clustering algorithms such as ensemble clustering [22][23][24] combine at least two kinds of the clustering algorithms mentioned above to get higher quality clustering results. Also, ensemble clustering algorithms using various strategies [25][26][27][28][29][30][31][32][33][34] to break through the limitations of base clustering algorithms have been increasingly popular in recent years.…”

Section: Data Object Clustering Methodsmentioning

confidence: 99%

Automatic Determination of Clustering Centers for “Clustering by Fast Search and Find of Density Peaks”

Min

Huang

Sheng

2020

Mathematical Problems in Engineering

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Dividing abstract object sets into multiple groups, called clustering, is essential for effective data mining. Clustering can find innate but unknown real-world knowledge that is inaccessible by any other means. Rodriguez and Laio have published a paper about a density-based fast clustering algorithm in Science called CFSFDP. CFSFDP is a highly efficient algorithm that clusters objects by using fast searching of density peaks. But with CFSFDP, the essential second step of finding clustering centers must be done manually. Furthermore, when the amount of data objects increases or a decision graph is complicated, determining clustering centers manually is difficult and time consuming, and clustering accuracy reduces sharply. To solve this problem, this paper proposes an improved clustering algorithm, ACDPC, that is based on data detection, which can automatically determinate clustering centers without manual intervention. First, the algorithm calculates the comprehensive metrics and sorts them based on the CFSFDP method. Second, the distance between the sorted objects is used to judge whether they are the correct clustering centers. Finally, the remaining objects are grouped into clusters. This algorithm can efficiently and automatically determine clustering centers without calculating additional variables. We verified ACDPC using three standard datasets and compared it with other clustering algorithms. The experimental results show that ACDPC is more efficient and robust than alternative methods.

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Section: Data Object Clustering Methodsmentioning

confidence: 99%

Automatic Determination of Clustering Centers for “Clustering by Fast Search and Find of Density Peaks”

Min

Huang

Sheng

2020

Mathematical Problems in Engineering

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“…Hence, the concept of ensemble clustering emerged such as with RCESCC [80], WOCCE [81], RCEIFBC [82]. The ensemble clustering approach calls for using more than one clustering approach at the same time and it fuses or aggregates their results in order to achieve more robust performance [80][81][82]. Such a category of approaches can combine our evolving algorithm with other clustering algorithms as the aggregated approach.…”

Section: Dbscanmentioning

confidence: 99%

Online Clustering of Evolving Data Streams Using a Density Grid-Based Method

et al. 2020

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In recent years, a significant boost in data availability for persistent data streams has been observed. These data streams are continually evolving, with the clusters frequently forming arbitrary shapes instead of regular shapes in the data space. This characteristic leads to an exponential increase in the processing time of traditional clustering algorithms for data streams. In this study, we propose a new online method, which is a density grid-based method for data stream clustering. The primary objectives of the density grid-based method are to reduce the number of distant function calls and to improve the cluster quality. The method is conducted entirely online and consists of two main phases. The first phase generates the Core Micro-Clusters (CMCs), and the second phase combines the CMCs into macro clusters. The gridbased method was utilized as an outlier buffer in order to handle multi-density data and noises. The method was tested on real and synthetic data streams employing different quality metrics and was compared with the popular method of clustering evolving data streams into arbitrary shapes. The proposed method was demonstrated to be an effective solution for reducing the number of calls to the distance function and improving the cluster quality. INDEX TERMS Clustering, data stream, evolving, grid-based method, core-micro-cluster, online.

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“…To improve the prediction ability of the ensemble model or reduce the prediction cost, it is necessary to screen the established sub-models and avoid multicollinearity in the sub-model as much as possible. As one of the main techniques of unsupervised machine learning, fuzzy clustering analysis had been widely used in large-scale data analysis, data mining, pattern recognition, and other fields [23][24][25]. The fuzzy C-means clustering was used to screen the sub-model in this study.…”

Section: Sub-model Discriminationmentioning

confidence: 99%

Prediction of the Solubility of CO2 in Imidazolium Ionic Liquids Based on Selective Ensemble Modeling Method

2020

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Solubility data is one of the essential basic data for CO2 capture by ionic liquids. A selective ensemble modeling method, proposed to overcome the shortcomings of current methods, was developed and applied to the prediction of the solubility of CO2 in imidazolium ionic liquids. Firstly, multiple different sub–models were established based on the diversities of data, structural, and parameter design philosophy. Secondly, the fuzzy C–means algorithm was used to cluster the sub–models, and the collinearity detection method was adopted to eliminate the sub–models with high collinearity. Finally, the information entropy method integrated the sub–models into the selective ensemble model. The validation of the CO2 solubility predictions against experimental data showed that the proposed ensemble model had better performance than its previous alternative, because more effective information was extracted from different angles, and the diversity and accuracy among the sub–models were fully integrated. This work not only provided an effective modeling method for the prediction of the solubility of CO2 in ionic liquids, but also provided an effective method for the discrimination of ionic liquids for CO2 capture.

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A fuzzy clustering ensemble based on cluster clustering and iterative Fusion of base clusters

Cited by 60 publications

References 52 publications

Automatic Determination of Clustering Centers for “Clustering by Fast Search and Find of Density Peaks”

Automatic Determination of Clustering Centers for “Clustering by Fast Search and Find of Density Peaks”

Online Clustering of Evolving Data Streams Using a Density Grid-Based Method

Prediction of the Solubility of CO2 in Imidazolium Ionic Liquids Based on Selective Ensemble Modeling Method

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