A distributed weighted possibilistic c-means algorithm for clustering incomplete big sensor data

International Journal of Distributed Sensor Networks

Volumn 2014, Issue , 2014, Pages

  Zhang, Qingchen ^a   Chen, Zhikui ^a  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

PATTERN RECOGNITION; SENSORS;

C-MEANS CLUSTERING ALGORITHM; CLOUD COMPUTING TECHNOLOGIES; CLUSTERING RESULTS; INCOMPLETE DATA; MISSING VALUES; PARTIAL DISTANCE; POSSIBILISTIC C-MEANS; POSSIBILISTIC C-MEANS CLUSTERING;

CLUSTERING ALGORITHMS;

EID: 84902167456     PISSN: 15501329     EISSN: 15501477     Source Type: Journal    
DOI: 10.1155/2014/430814     Document Type: Article

References (38)

1. Feki M. A., Kawsar F., Boussard M., Trappeniers L., The internet of things: the next technological revolution. Computer 2013 46 2 24 25 10.1109/MC.2013.63
2. Atzori L., Iera A., Morabito G., The internet of things: a survey. Computer Networks 2010 54 15 2787 2805 2-s2.0-77956877124 10.1016/j.comnet.2010. 05.010
3. Li M., Liu Y., Chen L., Nonthreshold-based event detection for 3D environment monitoring in sensor networks. IEEE Transactions on Knowledge and Data Engineering 2008 20 12 1699 1711 2-s2.0-55949105799 10.1109/TKDE.2008.114
4. Brass P., Bounds on coverage and target detection capabilities for models of networks of mobile sensors. ACM Transactions on Sensor Networks 2007 3 2, article 9 2-s2.0-34250312116 10.1145/1240226.1240229 1240229
5. Tan R., Xing G., Liu B., Wang J., Jia X., Exploiting data fusion to improve the coverage of wireless sensor networks. IEEE/ACM Transactions on Networking 2012 20 2 450 462 2-s2.0-84859964364 10.1109/TNET.2011.2164620
6. Wu X., Zhu X., Gong-Qing W., Ding W., Data mining with big data. IEEE Transactions on Knowledge and Data Engineering 2014 26 1 97 107 10.1109/TKDE.2013.109
7. Krishnapuram R., Keller J. M., A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems 1993 1 2 98 110 2-s2.0-0027595430 10.1109/91.227387
8. Zhang J.-S., Leung Y.-W., Improved possibilistic C-means clustering algorithms. IEEE Transactions on Fuzzy Systems 2004 12 2 209 217 2-s2.0-1942532748 10.1109/TFUZZ.2004.825079
9. Yang M.-S., Lai C.-Y., A robust automatic merging possibilistic clustering method. IEEE Transactions on Fuzzy Systems 2011 19 1 26 41 2-s2.0-79551622290 10.1109/TFUZZ.2010.2077640
10. Barni M., Cappellini V., Mecocci A., Comments on a possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems 1996 4 3 393 396 10.1109/91.531780
11. Pal N. R., Pal K., Keller J. M., Bezdek J. C., A possibilistic fuzzy C-means clustering algorithm. IEEE Transactions on Fuzzy Systems 2005 13 4 517 530 2-s2.0-26844532803 10.1109/TFUZZ.2004.840099
12. Xie Z., Wang S., Chung F. L., An enhanced possibilistic C-means clustering algorithm EPCM. Soft Computing-A Fusion of Foundations, Methodologies and Applications 2008 12 6 593 611 2-s2.0-38749134116 10.1007/s00500-007-0231-6
13. Li D., Gu H., Zhang L., A fuzzy C-means clustering algorithm based on nearest-neighbor intervals for incomplete data. Expert Systems with Applications 2010 37 10 6942 6947 2-s2.0-78649930585 10.1016/j.eswa.2010.03.028
14. Hathaway R. J., Bezdek J. C., Fuzzy C-means clustering of incomplete data. IEEE Transactions on Systems, Man, and Cybernetics, B: Cybernetics 2001 31 5 735 744 2-s2.0-0035481296 10.1109/3477.956035
15. Liu B., Xia S. X., Zhou Y., A sample-weighted possibilistic fuzzy clustering algorithm. Acta Electronica Sinica 2012 40 2 371 375
16. Schneider A., Weighted possibilistic C-means clustering algorithms. IEEE Transactions on Fuzzy Systems 2000 1 176 180 10.1109/FUZZY.2000.838654
17. Filippone M., Masulli F., Rovetta S., Applying the possibilistic C-means algorithm in kernel-induced spaces. IEEE Transactions on Fuzzy Systems 2010 18 3 572 584 2-s2.0-77953110999 10.1109/TFUZZ.2010.2043440
18. Armbrust M., Fox A., Griffith R., Joseph A. D., Katz R., Konwinski A., Lee G., Patterson D., Rabkin A., Stoica I., Zaharia M., A view of cloud computing. Communications of the ACM 2010 53 4 50 58 2-s2.0-77950347409 10.1145/1721654.1721672
19. Zhang Q., Chen Z., Yang L. T., A nodes scheduling model based on Markov chain prediction for big streaming data analysis. International Journal of Communication Systems 2014 10.1002/dac.2779
20. Dean J., Ghemawat S., MapReduce: simplified data processing on large clusters. Communications of the ACM 2008 51 1 107 113 2-s2.0-37549003336 10.1145/1327452.1327492
21. Zhao W., Ma H., He Q., Parallel k-means clustering based on mapreduce. Proceedings of the 1st International Conference on Cloud Computing 2009 Berlin, Germany Springer 674 679 10.1007/978-3-642-10665-1-71
22. Ene A., Im S., Moseley B., Fast clustering using MapReduce. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining August 2011 New York, NY, USA ACM 681 689 2-s2.0-80052666000 10.1145/2020408.2020515
23. Bahmani B., Moseley B., Vattani A., Kumar R., Vassilvitskii S., Scalable K-means++. Proceedings of the VLDB Endowment 2012 5 7 622 633
24. Sun T., Shu C., Li F., Yu H., Ma L., Fang Y., An efficient hierarchical clustering method for large datasets with map-reduce. Proceedings of the International Conference on Parallel and Distributed Computing, Applications and Technologies, (PDCAT '09) December 2009 Higashihiroshima, Japan 494 499 2-s2.0-77951019939 10.1109/PDCAT.2009.46
25. Gao H., Jiang J., She L., Fu Y., A new agglomerative hierarchical clustering algorithm implementation based on the map reduce framework. International Journal of Digital Content Technology and Its Applications 2010 4 3 95 100 2-s2.0-78651524475 10.4156/jdcta.vol4.issue3.9
26. Li L., Xi Y., Research on clustering algorithm and its parallelization strategy. Proceeding of the International Conference on Computational and Information Sciences (ICCIS '11) October 2011 Chengdu, China IEEE 325 328 2-s2.0-83755185529 10.1109/ICCIS.2011.223
27. He Y., Tan H., Luo W., Mr-dbscan: an efficient parallel density-based clustering algorithm using MapReduce. Proceeding of the 17th IEEE International Conference on Parallel and Distributed Systems December 2011 Tainan, Taiwan 473 480 10.1109/ICPADS.2011.83
28. Frey B. J., Dueck D., Clustering by passing messages between data points. Science 2007 315 5814 972 976 2-s2.0-33847172327 10.1126/science.1136800
29. Lu W., Chenyang D., Baogang W., Chunhui S., Zhenchao Y., Distributed affinity propagation clustering based on MapReduce. Journal of Computer Research and Development 2012 49 8 1762 1772
30. Yang K., He G., He G., Research and application of MapReduce-based MST text clustering algorithm. Proceedings of the IEEE International Conference on Information Science and Technology (ICIST '12) March 2012 IEEE 753 757
31. Yu Q., Dai M., Parallel fuzzy C-means algorithm based on MapReduce. Computer Engineering and Applications 2013 49 14 133 151
32. Papadimitriou S., Sun J., DisCo: distributed Co-clustering with map-reduce: a case study towards petabyte-scale end-to-end mining. Proceeding of the 8th IEEE International Conference on Data Mining, (ICDM '08) December 2008 Pisa, Italy IEEE 512 521 2-s2.0-67149126890 10.1109/ICDM.2008.142
33. Cordeiro R. L. F., Traina C. Jr., Traina A. J. M., López J., Kang U., Faloutsos C., Clustering very large multi-dimensional datasets with MapReduce. Proceeding of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining August 2011 ACM 690 698 2-s2.0-80052686089 10.1145/2020408.2020516
34. Lu L., Gu Y., Grossman R., dSimpleGraph: a novel distributed clustering algorithm for exploring very large scale unknown data sets. Proceeding of the10th IEEE International Conference on Data Mining Workshops (ICDMW '10) December 2010 Washington, DC, USA 162 169 2-s2.0-79951763568 10.1109/ICDMW.2010.12
35. Vergaraa A., Vembua S., Ayhanb T., Ryanc M. A., Homerc M. L., Huertaa R., Chemical gas sensor drift compensation using classifier ensembles. Sensors and Actuators B: Chemical 2012 166-167 320 329 10.1016/j.snb.2012.01.074
36. Rodriguez-Lujana I., Fonollosaa J., Vergarab A., Homerc M., Huertaa R., On the calibration of sensor arrays for pattern recognition using the minimal number of experiments. Chemometrics and Intelligent Laboratory Systems 2014 130 123 134 10.1016/j.chemolab.2013.10.012
37. Havens T. C., Bezdek J. C., Leckie C., Hall L. O., Palaniswami M., Fuzzy C-means algorithms for very large data. IEEE Transactions on Fuzzy Systems 2012 20 6 1130 1146 10.1109/TFUZZ.2012.2201485
38. Han X. D., Xia Z. X., Liu B., Zhou Y., Kernel-based fast improved possibilistic C-means clustering method. Computer Engineering and Applications 2011 47 6 176 180