Is there any recommended paper for creating fuzzy set rules from fuzzy clustering result?

Dr. Indrajit Mandal

Dear friend

Greetings.

For your reference, I have attached some of the references that may help you:

Scopus

EXPORT DATE:28 Dec 2014

Jain, A., Ramana Reddy, B.V.

Eigenvector centrality based cluster size control in randomly deployed wireless sensor networks

(2014) Expert Systems with Applications, 42 (5), pp. 2657-2669.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84918810716&partnerID=40&md5=c2a927c99e39e33cee2627a5dae367b9

DOI: 10.1016/j.eswa.2014.11.015

Stevenson, B.A., McNeill, S., Hewitt, A.E.

Characterising soil quality clusters in relation to land use and soil order in New Zealand: An application of the phenoform concept

(2015) Geoderma, 239, pp. 135-142.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84908341850&partnerID=40&md5=d205194c4c5b62a4e6a965e1fb54b128

DOI: 10.1016/j.geoderma.2014.10.003

Amirian, E., Leung, J.Y., Zanon, S., Dzurman, P.

Integrated cluster analysis and artificial neural network modeling for steam-assisted gravity drainage performance prediction in heterogeneous reservoirs

(2014) Expert Systems with Applications, 42 (2), pp. 723-740.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84907494097&partnerID=40&md5=33f62e79dea3526df8a1975a60eba45e

DOI: 10.1016/j.eswa.2014.08.034

Chu, H.-J., Huang, B., Lin, C.-Y.

Modeling the spatio-temporal heterogeneity in the PM10-PM2.5 relationship

(2015) Atmospheric Environment, 102, pp. 176-182.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84914164457&partnerID=40&md5=9137bd8a240616d4884427061484e542

DOI: 10.1016/j.atmosenv.2014.11.062

Natarajan, T., Rajendran, K.

Estimates of site response based on spectral ratio between horizontal and vertical components of ambient vibrations in the source zone of 2001 Bhuj earthquake

(2015) Journal of Asian Earth Sciences, 98, pp. 85-97.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84911879081&partnerID=40&md5=054d5f159b6b9b040f0f97f0577c9a8c

DOI: 10.1016/j.jseaes.2014.10.032

Shamshirband, S., Gocić, M., Petković, D., Javidnia, H., Ab Hamid, S.H., Mansor, Z., Qasem, S.N.

Clustering project management for drought regions determination: A case study in Serbia

(2015) Agricultural and Forest Meteorology, 200, pp. 57-65.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84907993839&partnerID=40&md5=4e8873db5680a4e9ab67eacf76d96844

DOI: 10.1016/j.agrformet.2014.09.020

Jote, N., Beshah, B., Kitaw, D.

Ethiopian livestock husbandry cluster identification using FUZZY-AHP approach

(2015) Advances in Intelligent Systems and Computing, 334, pp. 233-243.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84912016998&partnerID=40&md5=9bbedc5edd89c4cc094f5f51e924da4b

DOI: 10.1007/978-3-319-13572-4_19

Nadjafi, M., Farsi, M.A., Najafi, A.

Uncertainty improving in importance sampling: An integrated approach with fuzzy-cluster sampling

(2015) Safety and Reliability: Methodology and Applications - Proceedings of the European Safety and Reliability Conference, ESREL 2014, pp. 2107-2112.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84906658163&partnerID=40&md5=1704269cc1af83f6ab87431458a61b5f

Liu, X., Wang, X., Pedrycz, W.

Fuzzy clustering with semantic interpretation

(2015) Applied Soft Computing Journal, 26, pp. 21-30.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84908321064&partnerID=40&md5=3ce61d787615a9c9d3396d5b83608542

DOI: 10.1016/j.asoc.2014.09.037

Askari, S., Montazerin, N.

A high-order multi-variable Fuzzy Time Series forecasting algorithm based on fuzzy clustering

(2015) Expert Systems with Applications, 42 (4), pp. 2121-2135.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84910624398&partnerID=40&md5=565ccfc2c72993b81fee69cd90e40898

DOI: 10.1016/j.eswa.2014.09.036

Jain, M., Preeti

Availability analysis of software rejuvenation in active/standby cluster system

(2015) International Journal of Industrial and Systems Engineering, 19 (1), pp. 75-93.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84915747948&partnerID=40&md5=7087e24971cb3d8b313265346f936c3d

DOI: 10.1504/IJISE.2015.065948

Karami, A., Guerrero-Zapata, M.

A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks

(2015) Neurocomputing, 149 (PC), pp. 1253-1269.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84912115872&partnerID=40&md5=be35f481a6523b3a03859fe33b7c273f

DOI: 10.1016/j.neucom.2014.08.070

Nguyen, S.D., Nguyen, Q.H., Choi, S.-B.

Hybrid clustering based fuzzy structure for vibration control - Part 1: A novel algorithm for building neuro-fuzzy system

(2015) Mechanical Systems and Signal Processing, 50-51, pp. 510-525.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84905814810&partnerID=40&md5=99ae1aee97ac0a6c08196f84dd8edcc7

DOI: 10.1016/j.ymssp.2014.04.021

Thong, P.H., Le, H.S.

A new approach to multi-variable fuzzy forecasting using picture fuzzy clustering and picture fuzzy rule interpolation method

(2015) Advances in Intelligent Systems and Computing, 326, pp. 679-690.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84910665471&partnerID=40&md5=117cc1657be24ed2a7315c059be0da56

DOI: 10.1007/978-3-319-11680-8_54

Casabayó, M., Agell, N., Sánchez-Hernández, G.

Improved market segmentation by fuzzifying crisp clusters: A case study of the energy market in Spain

(2015) Expert Systems with Applications, 42 (3), pp. 1637-1643.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84908428476&partnerID=40&md5=a156e30f0af4cab08c6cf5eea12f82e7

DOI: 10.1016/j.eswa.2014.09.044

Srinivasa Rao, B., Srinivasa Reddy, E.

A novel fast FCM clustering for segmentation of salt and pepper noise corrupted images

(2015) Smart Innovation, Systems and Technologies, 32, pp. 381-391.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84917740428&partnerID=40&md5=9db76c7aa99be69095c44e9e1d7cdba6

DOI: 10.1007/978-81-322-2208-8_35

Samarasinghe, S., Chaiboonchoe, A.

Neural networks and fuzzy clustering methods for assessing the efficacy of microarray based intrinsic gene signatures in breast cancer classification and the character and relations of identified subtypes

(2015) Methods in Molecular Biology, 1260, pp. 285-317.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84917690959&partnerID=40&md5=aa89ec2b35f5df10da5b540a18629465

DOI: 10.1007/978-1-4939-2239-0_18

Wang, J.-S., Shen, N.-N., Han, S.

Multiple PSO-BP neural networks predictive model of grinding granularity based on fuzzy c-means clustering algorithm

(2015) ICIC Express Letters, Part B: Applications, 6 (1), pp. 127-132.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84918567064&partnerID=40&md5=83aba5f79af6ed063836e37b5b2b3f1d

Rastogi, R., Agarwal, S., Sharma, P., Kaul, U., Jain, S.

A novel D&C approach for efficient fuzzy unsupervised classification for mixed variety of data

(2015) Advances in Intelligent Systems and Computing, 338, pp. 553-563.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84915746430&partnerID=40&md5=7bf1086b0fe8bb2ae321240b0c73847b

DOI: 10.1007/978-3-319-13731-5_60

23rd International Conference on Systems Engineering, ICSEng 2014

(2015) Advances in Intelligent Systems and Computing, 1089, .

http://www.scopus.com/inward/record.url?eid=2-s2.0-84906536366&partnerID=40&md5=ab90169f33d535d47d75e743b52f0063

Dong, C.-R., Ng, W.W.Y., Wang, X.-Z., Chan, P.P.K., Yeung, D.S.

An improved differential evolution and its application to determining feature weights in similarity-based clustering

(2014) Neurocomputing, 146, pp. 95-103.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84906948389&partnerID=40&md5=032fc922fde2169f3cc51562094b0edd

DOI: 10.1016/j.neucom.2014.04.065

Peng, K., Li, X., Wang, Z.

Hydrochemical characteristics of groundwater movement and evolution in the Xinli deposit of the Sanshandao gold mine using FCM and PCA methods

(2014) Environmental Earth Sciences, 16 p. Article in Press.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84918562539&partnerID=40&md5=ce4da67677a61ec335ac2962a148ab14

DOI: 10.1007/s12665-014-3938-6

Altameem, T., Zanaty, E.A., Tolba, A.

A new fuzzy C-means method for magnetic resonance image brain segmentation

(2014) Connection Science, 17 p. Article in Press.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84916620229&partnerID=40&md5=f21c169fb3d9cc3351ee6ff67a9a0fd3

DOI: 10.1080/09540091.2014.970126

Abdelghaffar, N.M., Lotfy, H.M.S., Khamis, S.M.

A multi-agent-based approach for fuzzy clustering of large image data

(2014) Journal of Real-Time Image Processing, 13 p. Article in Press.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84914168973&partnerID=40&md5=1300add05b53fdf0645f4579e185c4de

DOI: 10.1007/s11554-014-0473-3

Zhou, J., Wang, Q., Hung, C.-C., Yang, F.

Credibilistic clustering algorithms via alternating cluster estimation

(2014) Journal of Intelligent Manufacturing, 12 p. Article in Press.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84914140062&partnerID=40&md5=c38d47db8f4866094f115d3f692632ec

DOI: 10.1007/s10845-014-1004-6

Wang, Y., Chen, L., Mei, J.-P.

Incremental fuzzy clustering with multiple medoids for large data

(2014) IEEE Transactions on Fuzzy Systems, 22 (6), art. no. 6704313, pp. 1557-1568.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84914147924&partnerID=40&md5=c7e99663ddd5a67352c9f1afb1d1a826

DOI: 10.1109/TFUZZ.2014.2298244

Verma, N.K., Roy, A.

Self-optimal clustering technique using optimized threshold function

(2014) IEEE Systems Journal, 8 (4), art. no. 6555929, pp. 1213-1226.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84913556309&partnerID=40&md5=013a5a11c1fc16966ae68534e31ab3a5

DOI: 10.1109/JSYST.2013.2261231

Li, X., Ren, J., Zhao, C., Qiao, T., Marshall, S.

Novel multivariate vector quantization for effective compression of hyperspectral imagery

(2014) Optics Communications, 332, pp. 192-200.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84904985072&partnerID=40&md5=c34770ecd310679a0e822e03bde15e23

DOI: 10.1016/j.optcom.2014.07.011

Liu, R., Zhang, L., Li, B., Ma, Y., Jiao, L.

Synergy of two mutations based immune multi-objective automatic fuzzy clustering algorithm

(2014) Knowledge and Information Systems, 25 p. Article in Press.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84911377356&partnerID=40&md5=e9358990ea039f619c374ce1282a3bc5

DOI: 10.1007/s10115-014-0805-4

Kim, D., Jin, C.-S., Ho, C.-H., Kim, J., Kim, J.-H.

Climatological features of WRF-simulated tropical cyclones over the western North Pacific

(2014) Climate Dynamics, 13 p. Article in Press.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84911426244&partnerID=40&md5=93b06166061897e8bbc5b9578d7259ab

DOI: 10.1007/s00382-014-2410-3

Rodger, J.A.

Application of a Fuzzy Feasibility Bayesian Probabilistic Estimation of supply chain backorder aging, unfilled backorders, and customer wait time using stochastic simulation with Markov blankets

(2014) Expert Systems with Applications, 41 (16), pp. 7005-7022.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84903954851&partnerID=40&md5=e8442d6239fa77af955e4b796406d895

DOI: 10.1016/j.eswa.2014.05.012

Niros, A.D., Tsekouras, G.E., Tsolakis, D., Manousakis-Kokorakis, A., Kyriazis, D.

Hierarchical Fuzzy Clustering in Conjunction with Particle Swarm Optimization to Efficiently Design RBF Neural Networks

(2014) Journal of Intelligent and Robotic Systems: Theory and Applications, 21 p. Article in Press.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84910089310&partnerID=40&md5=a0653446c7d52bc7cc6cbd62b48e29cf

DOI: 10.1007/s10846-014-0152-4

Benaichouche, A.N., Oulhadj, H., Siarry, P.

Multiobjective improved spatial fuzzy c-means clustering for image segmentation combining Pareto-optimal clusters

(2014) Journal of Heuristics, 22 p. Article in Press.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84910091355&partnerID=40&md5=fddceea69d66fcefd8d9f35e825adf41

DOI: 10.1007/s10732-014-9267-9

Yang, J., Shi, H., Feng, B., Zhao, L., Ma, C., Mei, X.

Thermal error modeling and compensation for a high-speed motorized spindle

(2014) International Journal of Advanced Manufacturing Technology, 13 p. Article in Press.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84908430957&partnerID=40&md5=7d12d268dd8df853b275a43df61bc9de

DOI: 10.1007/s00170-014-6535-7

Roh, S.-B., Pedrycz, W., Ahn, T.-C.

A design of granular fuzzy classifier

(2014) Expert Systems with Applications, 41 (15), pp. 6786-6795.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84902675014&partnerID=40&md5=e86130039a2d15b718bef14c133f0a37

DOI: 10.1016/j.eswa.2014.04.040

France, S.L., Batchelder, W.H.

Unsupervised consensus analysis for on-line review and questionnaire data

(2014) Information Sciences, 283, pp. 241-257.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84906089762&partnerID=40&md5=1721ea0a7c32c9d04611f40057b4eb01

DOI: 10.1016/j.ins.2014.06.015

Chang, D., Zhang, J., Zhu, L., Ge, S.-H., Li, P.-Y., Liu, G.-S.

Delineation of management zones using an active canopy sensor for a tobacco field

(2014) Computers and Electronics in Agriculture, 109, pp. 172-178.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84908383421&partnerID=40&md5=596e54a31805b9d319e90ffac515bc23

DOI: 10.1016/j.compag.2014.09.019

Andrews, J.T., Vogt, C.

Source to sink: Statistical identification of regional variations in the mineralogy of surface sediments in the western Nordic Seas (58°N-75°N; 10°W-40°W)

(2014) Marine Geology, 357, pp. 151-162.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84906877505&partnerID=40&md5=a477e16deb8649d0e2291c70b8db2b86

DOI: 10.1016/j.margeo.2014.08.005

Cocaña-Fernández, A., Ranilla, J., Sánchez, L.

Energy-efficient allocation of computing node slots in HPC clusters through parameter learning and hybrid genetic fuzzy system modeling

(2014) Journal of Supercomputing, 12 p. Article in Press.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84908312548&partnerID=40&md5=ea9c46a68800e55ecb41d966c7614788

DOI: 10.1007/s11227-014-1320-9

Yuen, K.K.F.

A hybrid fuzzy quality function deployment framework using cognitive network process and aggregative grading clustering: An application to cloud software product development

(2014) Neurocomputing, 142, pp. 95-106.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84904365394&partnerID=40&md5=e77d24811062f2fb178e755860d491ec

DOI: 10.1016/j.neucom.2014.03.045

Tlili, M., Ayadi, T., Hamdani, T.M., Alimi, A.M.

Performance evaluation of FMIG clustering using fuzzy validity indexes

(2014) Soft Computing, 14 p. Article in Press.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84908124130&partnerID=40&md5=bce77c51361b5333101ebbb010fab14e

DOI: 10.1007/s00500-014-1478-3

Zhang, D., Ji, M., Yang, J., Zhang, Y., Xie, F.

A novel cluster validity index for fuzzy clustering based on bipartite modularity

(2014) Fuzzy Sets and Systems, 253, pp. 122-137.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84905909286&partnerID=40&md5=b9ed6ae5e0eb9b487b78cd1d6ad4f24d

DOI: 10.1016/j.fss.2013.12.013

Zhou, D., Zhou, H.

A modified strategy of fuzzy clustering algorithm for image segmentation

(2014) Soft Computing, 12 p. Article in Press.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84907904124&partnerID=40&md5=bbe48490d4815905fdfed48db1b3be1e

DOI: 10.1007/s00500-014-1481-8

Sabit, H., Al-Anbuky, A.

Multivariate spatial condition mapping using subtractive fuzzy cluster means

(2014) Sensors (Switzerland), 14 (10), pp. 18960-18981.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84908544125&partnerID=40&md5=fdd92d95873396108ca0a35419863828

DOI: 10.3390/s141018960

Yin, S., Huang, Z.

Performance Monitoring for Vehicle Suspension System via Fuzzy Positivistic C-Means Clustering Based on Accelerometer Measurements

(2014) IEEE/ASME Transactions on Mechatronics, . Article in Press.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84907895490&partnerID=40&md5=f9d030f51a0c08caf39a11cfe54e8d8b

DOI: 10.1109/TMECH.2014.2358674

Kim, J.W., Ha, S.H.

Price comparisons on the internet based on computational intelligence

(2014) PLoS ONE, 9 (9), art. no. e106946, .

http://www.scopus.com/inward/record.url?eid=2-s2.0-84907484375&partnerID=40&md5=08b14d28a933abb84ca866a7fa21bc20

DOI: 10.1371/journal.pone.0106946

Sanchez, M.A., Castillo, O., Castro, J.R., Melin, P.

Fuzzy granular gravitational clustering algorithm for multivariate data

(2014) Information Sciences, 279, pp. 498-511.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84901831650&partnerID=40&md5=3ad45083d120ac3e323c9d2c79fce200

DOI: 10.1016/j.ins.2014.04.005

Konecny, J., Osicka, P.

Triadic concept lattices in the framework of aggregation structures

(2014) Information Sciences, 279, pp. 512-527.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84901855357&partnerID=40&md5=ca5720aa9e21ade24e1ac1875e4aebc8

DOI: 10.1016/j.ins.2014.04.006

Rostam Niakan Kalhori, M., Fazel Zarandi, M.H., Turksen, I.B.

A new credibilistic clustering algorithm

(2014) Information Sciences, 279, pp. 105-122.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84901818992&partnerID=40&md5=30a6b037b83b6e0d590941d3e24d0cc8

DOI: 10.1016/j.ins.2014.03.106

Alsmadi, M.K.

A hybrid firefly algorithm with fuzzy-c mean algorithm for MRI brain segmentation

(2014) American Journal of Applied Sciences, 11 (9), pp. 1676-1691.

http://www.scopus.com/inward/record.url?eid=2-s2.0-84907554314&partnerID=40&md5=160e94f08920d7a13f5bddac7cc4cae8

DOI: 10.3844/ajassp.2014.1676.1691

I hope this helps you.

Best regards

Dr.Indrajit Mandal.Ph.D.

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