Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Automatic Control and Computer Sciences
Erschienen in: Automatic Control and Computer Sciences 1/2020

01.01.2020

Fuzzy C-Means on Metric Lattice

verfasst von: X. Meng, M. Liu, H. Zhou, J. Wu, F. Xu, Q. Wu

Erschienen in: Automatic Control and Computer Sciences | Ausgabe 1/2020

Einloggen, um Zugang zu erhalten

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

This work proposes a new clustering algorithm named FINFCM by converting original data into fuzzy interval number (FIN) firstly, then it proofs F that denotes the collection of FINs is a lattice and introduce a novel metric distance based on the results from lattice theory as well as combining them with Fuzzy c-means clustering. The relevant mathematical background about lattice theory and the specific procedure which is used to construct FIN have been presented in this paper. Three evaluation indexes including Compactness, RandIndex and Precision are applied to evaluate the performance of FINFCM, FCM and HC algorithm in four experiments used UCI public datasets. The FINFCM algorithm has shown better clustering performance compared to other traditional clustering algorithms and the results are also discussed specifically.
Vorheriger Artikel Disturbance Observer-Based Control of Master and Slave Systems with Input Saturation
Nächster Artikel Fault Location in the Transmission Network Using Artificial Neural Network
Literatur
1.
Zhu, J. and Wang, H., An improved K-means clustering algorithm, Proc. 2nd IEEE Conf. on Information Management and Engineering, Chengdu, 2010.
2.
Bouguettaya, A., Yu, Q., Liu, X.M., Zhou, X.M., and Song, A., Efficient agglomerative hierarchical clustering, Expert Syst. Appl., 2015, vol. 42, no. 5, pp. 2785–2797.CrossRef
3.
Guler, C. and Thyne, G.D., Delineation of hydrochemical facies distribution in a regional groundwater system by means of fuzzy c-means clustering, Water Resour. Res., 2004, vol. 40, no.12, p. 87.CrossRef
4.
Zadeh, L.A., Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems, World Scientific Publishing, 1996.CrossRef
5.
Kaburlasos, V.G., Petridis, V., Brett, P.N., and Baker, D.A., Estimation of the stapes-bone thickness in stapedotomy surgical procedure using a machine-learning technique, IEEE Trans. Inf. Technol. Biomed., 1999, vol. 3, no. 4, pp. 268–277.CrossRef
6.
Kaburlasos, V.G. and Petridis, V., Fuzzy lattice neurocomputing (FLN): A novel connectionist scheme for versatile learning and decision making by clustering, Int. J. Comput. Their Appl., 1997, vol. 4, no. 2, pp. 31–43.
7.
Kaburlasos, V.G. and Petridis, V., Fuzzy lattice neurocomputing (FLN) models, Neural Networks, 2000, vol. 13, no. 10, pp. 1145–1170.CrossRef
8.
Kaburlasos, V.G. and Petridis, V., New Learning Paradigms in Soft Computings,Physica, 2002.
9.
Petridis, V. and Kaburlasos, V.G., Fuzzy lattice neural network (FLNN): A hybrid model for learning, IEEE Trans. Neural Networks, 1998, vol. 9, no. 5, pp. 877–890.CrossRef
10.
Petridis, V. and Kaburlasos, V.G., Learning in the framework of fuzzy lattices, IEEE Trans. Fuzzy Syst., 1999, vol. 7, no. 4, pp. 422–440.CrossRef
11.
Petridis, V. and Kaburlasos, V.G., Clustering and classification in structured data domains using Fuzzy Lattice Neurocomputing (FLN), IEEE Trans. Knowl. Data Eng., 2001, vol. 13, no. 2, pp. 245–260.CrossRef
12.
Zhang, H.Y., Yang, S.Y. and Yue, Z.W., On inclusion measures of intuitionistic and interval-valued intuitionistic fuzzy values and their applications to group decision making, Int. J. Mach. Learn. Cybern., 2016, vol. 7, no. 5, pp. 833–843.CrossRef
13.
Jamshidi, Y., A nearest neighbour classifier based on probabilistically/possibilistically intervals' number for spam filtering, Int. J. Soft Comput. Networking, 2016, vol. 1, no. 1, pp. 4–16.CrossRef
14.
Kaburlasos, V.G. and Kehagias, A., Fuzzy inference system (FIS) extensions based on the lattice theory, IEEE Trans. Fuzzy Syst., 2014, vol. 22, no. 3, pp. 531–546.CrossRef
15.
Liu, H.B., Xiong, S.W., and Fang, Z.X., FL-GrCCA: A granular computing classification algorithm based on fuzzy lattices, Comput. Math. Appl., 2011, vol. 61, no. 1, pp. 138–147.MathSciNetCrossRef
16.
Liu, H.B., Xiong, S.W., and Wu, C., Hyperspherical granular computing classification algorithm based on fuzzy lattices, Math. Comput. Modell., 2013, vol. 57, nos. 3–4, pp. 661–670.MathSciNetCrossRef
17.
Khezeli, Y.J. and Nezamabadi-pour, H., Fuzzy lattice reasoning for pattern classification using a new positive valuation function, Adv. Fuzzy Syst., 2012, vol. 8, no. 4, p. 8.MathSciNetMATH
18.
Fong, S., Mohammed, S., Fiaidhi, J., and Kwoh, C.K., Using causality modeling and Fuzzy Lattice Reasoning algorithm for predicting blood glucose, Expert Syst. Appl., 2013, vol. 40, no. 18, pp. 7354–7366.CrossRef
19.
Kaburlasos, V.G., Papadakis, S.E., and Amanatiadis, A., Binary image 2D shape learning and recognition based on lattice-computing (LC) techniques, J. Math. Imaging Vision, 2012, vol. 42, nos. 2–3, pp. 118–133.MathSciNetCrossRef
20.
Papakostas, G.A. and Kaburlasos, V.G., Lattice computing (LC) meta-representation for pattern classification, Proc. IEEE Conf. on Fuzzy Systems, Beijing, 2014.
Metadaten
Titel
Fuzzy C-Means on Metric Lattice
verfasst von
X. Meng
M. Liu
H. Zhou
J. Wu
F. Xu
Q. Wu
Publikationsdatum
01.01.2020
Verlag
Pleiades Publishing
Erschienen in
Automatic Control and Computer Sciences / Ausgabe 1/2020
Print ISSN: 0146-4116
Elektronische ISSN: 1558-108X
DOI
https://doi.org/10.3103/S0146411620010071

Weitere Artikel der Ausgabe 1/2020

Automatic Control and Computer Sciences 1/2020 Zur Ausgabe

OriginalPaper

A Novel Hybrid Intrusion Detection Using Flow-Based Anomaly Detection and Cross-Layer Features in Wireless Sensor Network

OriginalPaper

Personal-Bullying Detection Based on Multi-Attention and Cognitive Feature

OriginalPaper

An Adaptive Vibe Algorithm Based on Dispersion Coefficient and Spatial Consistency Factor

OriginalPaper

Fault Location in the Transmission Network Using Artificial Neural Network

OriginalPaper

Active Control Design Approach for Roll/Yaw Attitude Satellite Stabilization with Flexible Vibration

OriginalPaper

Robust Filtering for Discrete Systems with Unknown Inputs and Jump Parameters

Neuer Inhalt

    Bildnachweise