Clustering is one the main area in data mining
literature. There are various algorithms for clustering. There
are several clustering approaches available in the literature
to cluster the document. But most of the existing clustering
techniques suffer from a wide range of limitations. The
existing clustering approaches face the issues like practical
applicability, very less accuracy, more classification time etc.
In recent times, inclusion of fuzzy logic in clustering results in
better clustering results. One of the widely used fuzzy logic
based clustering is Fuzzy C-Means (FCM) Clustering. In
order to further improve the performance of clustering, this
thesis uses Modified Fuzzy C-Means (MFCM) Clustering.
Before clustering, the documents are ranked using Term
Frequency–Inverse Document Frequency (TF–IDF)
technique. From the experimental results, it can be observed
that the proposed technique results in better clustering
results when compared to the existing technique.
M. Kishore Babu : Assistant Professor, Department of CSE,
Universal College of Engineering & Technology,
Guntur, AP, India.
Fuzzy C-means Clustering, Datasets, and Multi
View Point Clustering
Clustering decides the connections between information
objects in the data source. The things are arranged or
arranged based on the key of “maximizing the infraclass
similarity and reducing the interclass similarity”. It
discovers out something useful from data source.
Clustering has its roots in many areas, such as information
exploration, research, biology, and device learning etc.
Clustering methods can be divided into various types:
Dividing methods, Hierarchical methods, Solidity centered
methods, Grid-based methods; Design centered methods,
Probabilistic methods, and Chart theoretic and Unclear
methods. The Powerful mean algorithm are the significant
concentrate of this dissertation work. Dynamic mean
criteria generate good groups automatically because there
is no need to described the number of groups before head
but in Powerful mean criteria each data factor can be a
participant of one and only one group at a time. In other
terms we can say that the sum of account grades of each
information point in all groups is similar to one and in all
the staying groups its account quality is zero .In our thesis
dynamic criteria is customized using fuzzy criteria. By
implementing fuzzy criteria over Powerful criteria we can
show the account of each information factor in all groups
.By applying Unclear criteria over Powerful criteria
clustering can be at an extremely quicker rate. It is
appropriate to a large amount of information saved in
databases. The overall results are significant in displaying
that Powerful criteria display membership of each
information factor in every groups.
[1] X. Wu, V. Kumar, J.R. Quinlan, J. Ghosh, Q. Yang, H.
Motoda, G.J. McLachlan, A. Ng, B. Liu, P.S. Yu, Z.-H.
Zhou, M. Steinbach, D.J. Hand, and D. Steinberg, “Top
10 Algorithms in Data Mining,” Knowledge
Information Systems, vol. 14, no. 1, pp. 1-37, 2007.
[2] I. Guyon, U.V. Luxburg, and R.C. Williamson,
“Clustering:Science or Art?,” Proc. NIPS Workshop
Clustering Theory, 2009.
[3] I. Dhillon and D. Modha, “Concept Decompositions
for Large Sparse Text Data Using Clustering,” Machine Learning, vol. 42, nos. 1/2, pp. 143-175, Jan.
2001.
[4] S. Zhong, “Efficient Online Spherical K-means
Clustering,” Proc. IEEE Int’l Joint Conf. Neural
Networks (IJCNN), pp. 3180-3185, 2005.
[5] A. Banerjee, S. Merugu, I. Dhillon, and J. Ghosh,
“Clustering with Bregman Divergences,” J. Machine
Learning Research, vol. 6, pp. 1705-1749, Oct. 2005.
[6] E. Pekalska, A. Harol, R.P.W. Duin, B. Spillmann, and
H. Bunke, “Non-Euclidean or Non-Metric Measures
Can Be Informative,” Structural, Syntactic, and
Statistical Pattern Recognition, vol. 4109, pp. 871-880,
2006.
[7] M. Pelillo, “What Is a Cluster? Perspectives from
Game Theory,” Proc. NIPS Workshop Clustering
Theory, 2009.
[8] D. Lee and J. Lee, “Dynamic Dissimilarity Measure for
Support Based Clustering,” IEEE Trans. Knowledge
and Data Eng., vol. 22, no. 6, pp. 900-905, June 2010.
[9] A. Banerjee, I. Dhillon, J. Ghosh, and S. Sra,
“Clustering on the Unit Hypersphere Using Von Mises-
Fisher Distributions,” J. Machine Learning Research,
vol. 6, pp. 1345-1382, Sept. 2005.
[10] Mr.Kamakshaiah K, Dr.R..Seshadri “Water Quality
Analysis Using Enhanced K-Means Clustering"
International Journal of Advanced Research in
Computer Science and Software Engineering, Volume
5, Issue 10 October (2015) ISSN : 2277-128X
[11] T. Q. Chen and Y. Lu, “Color image segmentation an
innovative approach”, Pattern recognition, vol. 35,
2002, pp. 395-405.
[12] Y. Yang, C. Zheng, and P. Lin, “Fuzzy c-means
clustering algorithm with a novel penalty term for
image segmentation” Optoelectronic review, Vol.13,
Issue 4, 2005, pp. 309-315.
[13] Mr. Kamakshaiah K, Dr.R..Seshadri “Ground Water
Quality Assessment Using Data Mining Techniques”
International journal of Computer Applications
Volume 76-No 15, August 2013, ISSN: 0975-8887.
[14] Mr.Kamakshaiah K, Dr.R..Seshadri, “Classification
Ground Water Process Using PC Based K-Means
Clustering ", International Journal of Applied Sciences,
Engineering and Management, Vol 3,-No61,
November 2014,ISSN: 2320-3439.
