26-03-2014, 04:35 PM
Comparative Analysis of FCM and HCM Algorithm on Iris Data Set
ABSTRACT
Clustering is a primary data description method in data mining
which group’s most similar data. The data clustering is an
important problem in a wide variety of fields. Including data
mining, pattern recognition, and bioinformatics. There are
various algorithms used to solve this problem. This paper
presents the comparison of the performance analysis of Fuzzy C
mean (FCM) clustering algorithm and compares it with Hard C
Mean (HCM) algorithm on Iris flower data set. We measure
Time complexity and space Complexity of FCM and HCM at
Iris data [1] set. FCM clustering [2, 3] is a clustering technique
which is separated from Hard C Mean that employs hard
partitioning. The FCM employs fuzzy portioning such that a
point can belong to all groups with different membership grades
between 0 and 1.
Result after implementation of FCM &
HCM on Iris Data set:
The implementation of FCM & HCM is done on iris Data set in
MATLAB. The data set contains 3 classes of 50 instances each,
where each class refers to a type of iris plant. One class is
linearly separable from the other two, the latter are NOT linearly
separable from each other. The data set contain four attribute
which are given below
CONCLUSION
In partitioning based clustering algorithms, the number of final
cluster (k) needs to be defined beforehand. Also, algorithms
have problems like susceptible to local optima, sensitive to
outliers, memory space and unknown number of iteration steps
required to cluster. The time complexity of the HCM is O(ncdi)
The memory complexity of HCM is cd and the input output
complexity will be O(ndi). Fuzzy clustering, which constitute
the oldest component of soft computing, are suitable for
handling the issues related to understandability of patterns,
incomplete/noisy data, mixed media information and human
interaction, and can provide approximate solutions faster. They
have been mainly used in discovering association rules and
functional dependencies and image retrieval. The time
complexity of the Fuzzy C Mean algorithm is O(ndc2i). The
memory complexity of FCM is O(nd + nc),and the disk input
output complexity will be O(ndi)