Adaptive fuzzy exponent cluster ensemble system based feature selection and spectral clustering

Ayed, Abdelkarim Ben; Halima, Mohamed Ben; Alimi, Adel M.

doi:10.1109/fuzz-ieee.2017.8015721

Cited by 12 publications

(3 citation statements)

References 24 publications

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“…In our clustering experiments, we used 6 datasets from the UCI Machine Learning Repository [63] under noise free environment. These data illustrated in Table XIII are also used to compare our approach with other unsupervised clustering algorithms like Fuzzy k-means (FKM), Cluster Forest (CF) [69], Type-1 and Type-2 Fuzzy Cmeans (FCM and FCM2 respectively) [62], Bagged Clustering (BC2) [64], Evidence Accumulation (EA) [65] and Random Projection (RP) [66]. As cited in [69], there exist some metrics that can be used for clustering quality comparison.…”

Section: Clustering Resultsmentioning

confidence: 99%

“…XII represents the comparison between the same clustering methods above-cited for the metric C. All results for BC2, EA and RP were taken from[70]. Results for Cluster Forest and those for Type-2 Fuzzy C-means were derived from[69] and[62], respectively. As described in Table XI and XII, the findings, obtained for S1 and S2 measures, show relevant and significant results reaching 83% and 86% for the metric R and C, respectively.…”

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confidence: 99%

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Novel Intuitionistic-Based Interval Type-2 Fuzzy Similarity Measures With Application to Clustering

Cherif¹,

Baklouti

Hagras

et al. 2022

IEEE Trans. Fuzzy Syst.

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Similarity measures have been widely used in applications dealing with reasoning, classification and information retrieval. In this paper, we first propose three new Interval Type-2 Fuzzy Similarity measures (IT-2 FSMs) as a dual concept of some semi-metric distances between Intuitionistic Fuzzy Sets (IFSs). We also prove that the extended IT-2 FSMs satisfy many common properties (i.e. reflexivity, transivity, symmetry and overlapping). Experiments are carried out on a variety of datasets including UCI Learning Machine and real data. Comparative studies between the proposed IT-2 FSMs and the other well-known existing similarity measures (Gorzalczany, Bustince, Mitchell, Zeng and Li as well as VSM and Jaccard) are performed. Obviously, the best results are obtained with the IT-2 FSMs being resilient to the high levels of uncertainty noise. We also prove that our IT-2 FSMs can overcome the drawbacks of some existing similarity measures based on the accuracy rate measure. In addition, the proposed IT-2 FSMs are joined with Fuzzy cmeans algorithm as a clustering method and the proposed system is compared against the existing clustering algorithms (Type-1 Fuzzy k-means, Type-1 and Type-2 Fuzzy c-means, Cluster Forest, Bagged Clustering, Evidence Accumulation and Random Projection). Relying on the clustering quality parameters R and C (equivalent to the standard classification accuracy), the advanced IT-2FSMs show higher classification accuracy of about 86% which outperforms nearly the other classifiers.

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Section: Clustering Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Novel Intuitionistic-Based Interval Type-2 Fuzzy Similarity Measures With Application to Clustering

Cherif¹,

Baklouti

Hagras

et al. 2022

IEEE Trans. Fuzzy Syst.

Self Cite

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show abstract

“…For the long geological data acquisition [7], the population of reflection and wireless sensor network (WSN) are suitable [8]. In this article, some sensor networks can be used for the actual design and implementation of the complete wireless sensor network architecture.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Cluster in Wireless Sensor Network Using Life Time Delay Clustering Algorithms

Kumar¹,

Jayapandian²,

Balasubramanie³

2022

Computer Systems Science and Engineering

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Through Wireless Sensor Networks (WSN) formation, industrial and academic communities have seen remarkable development in recent decades. One of the most common techniques to derive the best out of wireless sensor networks is to upgrade the operating group. The most important problem is the arrangement of optimal number of sensor nodes as clusters to discuss clustering method. In this method, new client nodes and dynamic methods are used to determine the optimal number of clusters and cluster heads which are to be better organized and proposed to classify each round. Parameters of effective energy use and the ability to decide the best method of attachments are included. The Problem coverage find change ability network route due to which traffic and delays keep the performance to be very high. A newer version of Gravity Analysis Algorithm (GAA) is used to solve this problem. This proposed new approach GAA is introduced to improve network lifetime, increase system energy efficiency and end delay performance. Simulation results show that modified GAA performance is better than other networks and it has more advanced Life Time Delay Clustering Algorithms-LTDCA protocols. The proposed method provides a set of data collection and increased throughput in wireless sensor networks.

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Measuring Plantar Temperature Changes in Thermal Images Using Basic Statistical Descriptors

Filipe

Teixeira

2021

Computational Science and Its Applications – ICCSA 2021

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Adaptive fuzzy exponent cluster ensemble system based feature selection and spectral clustering

Cited by 12 publications

References 24 publications

Novel Intuitionistic-Based Interval Type-2 Fuzzy Similarity Measures With Application to Clustering

Novel Intuitionistic-Based Interval Type-2 Fuzzy Similarity Measures With Application to Clustering

Energy-Efficient Cluster in Wireless Sensor Network Using Life Time Delay Clustering Algorithms

Measuring Plantar Temperature Changes in Thermal Images Using Basic Statistical Descriptors

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