96,464 research outputs found
Empirical analysis of rough set categorical clustering techniques based on rough purity and value set
Clustering a set of objects into homogeneous groups is a fundamental operation
in data mining. Recently, attention has been put on categorical data clustering,
where data objects are made up of non-numerical attributes. The implementation of
several existing categorical clustering techniques is challenging as some are unable
to handle uncertainty and others have stability issues. In the process of dealing
with categorical data and handling uncertainty, the rough set theory has become
well-established mechanism in a wide variety of applications including databases.
The recent techniques such as Information-Theoretic Dependency Roughness (ITDR),
Maximum Dependency Attribute (MDA) and Maximum Significance Attribute (MSA)
outperformed their predecessor approaches like Bi-Clustering (BC), Total Roughness
(TR), Min-Min Roughness (MMR), and standard-deviation roughness (SDR). This
work explores the limitations and issues of ITDR, MDA and MSA techniques on
data sets where these techniques fails to select or faces difficulty in selecting their
best clustering attribute. Accordingly, two alternative techniques named Rough Purity
Approach (RPA) and Maximum Value Attribute (MVA) are proposed. The novelty
of both proposed approaches is that, the RPA presents a new uncertainty definition
based on purity of rough relational data base whereas, the MVA unlike other rough
set theory techniques uses the domain knowledge such as value set combined with
number of clusters (NoC). To show the significance, mathematical and theoretical
basis for proposed approaches, several propositions are illustrated. Moreover, the
recent rough categorical techniques like MDA, MSA, ITDR and classical clustering
technique like simple K-mean are used for comparison and the results are presented
in tabular and graphical forms. For experiments, data sets from previously utilized
research cases, a real supply base management (SBM) data set and UCI repository
are utilized. The results reveal significant improvement by proposed techniques for
categorical clustering in terms of purity (21%), entropy (9%), accuracy (16%), rough
accuracy (11%), iterations (99%) and time (93%).
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Rough clustering for web transactions
Grouping web transactions into clusters is important in order to obtain better
understanding of user's behavior. Currently, the rough approximation-based
clustering technique has been used to group web transactions into clusters. It is based
on the similarity of upper approximations of transactions by given threshold.
However, the processing time is still an issue due to the high complexity for finding
the similarity of upper approximations of a transaction which used to merge between
two or more clusters. In this study, an alternative technique for grouping web
transactions using rough set theory is proposed. It is based on the two similarity
classes which is nonvoid intersection. The technique is implemented in MATLAB
®
version 7.6.0.324 (R2008a). The two UCI benchmark datasets taken from:
http:/kdd.ics.uci.edu/ databases/msnbc/msnbc.html and
http:/kdd.ics.uci.edu/databases/ Microsoft / microsoft.html are opted in the
simulation processes. The simulation reveals that the proposed technique
significantly requires lower response time up to 62.69 % and 66.82 % as compared to
the rough approximation-based clustering, severally. Meanwhile, for cluster purity it
performs better until 2.5 % and 14.47%, respectively
Gabor Filter and Rough Clustering Based Edge Detection
This paper introduces an efficient edge detection method based on Gabor
filter and rough clustering. The input image is smoothed by Gabor function, and
the concept of rough clustering is used to focus on edge detection with soft
computational approach. Hysteresis thresholding is used to get the actual
output, i.e. edges of the input image. To show the effectiveness, the proposed
technique is compared with some other edge detection methods.Comment: Proc. IEEE Conf. #30853, International Conference on Human Computer
Interactions (ICHCI'13), Chennai, India, 23-24 Aug., 201
Autonomous clustering using rough set theory
This paper proposes a clustering technique that minimises the need for subjective
human intervention and is based on elements of rough set theory. The proposed algorithm is
unified in its approach to clustering and makes use of both local and global data properties to
obtain clustering solutions. It handles single-type and mixed attribute data sets with ease and
results from three data sets of single and mixed attribute types are used to illustrate the
technique and establish its efficiency
Study on the Rough-set-based Clustering Algorithm for Sensor Networks
The traditional clustering algorithm is a very typical level routing algorithm in wireless sensor networks (WSN). On the basis of the classical LEACH (Low Energy Adaptive Clustering Hierarchy) algorithm, this paper proposes an energy efficient clustering algorithm in WSN. Through the introduction of rough set, the new algorithm mainly introduces how to confirm an optimized strategy to choose the cluster head effectively by the simplified decision table. That is to say, by discrete normalized data preprocessing of attribute value, getting discretization decision table. Finally, the results from simulated experiments show that the clustering algorithm based on rough set theory can optimize the clustering algorithm in network data. That is to say, the rough-set-based clustering algorithm can effectively choose the cluster head, balance the energy of the nodes in the cluster and prolong the lifetime of sensor networks
An augmented space recursion study of the electronic structure of rough epitaxial overlayers
In this communication we propose the use of the Augmented Space Recursion as
an ideal methodology for the study of electronic and magnetic structures of
rough surfaces, interfaces and overlayers. The method can take into account
roughness, short-ranged clustering effects, surface dilatation and
interdiffusion. We illustrate our method by an application of Fe overlayer on
Ag (100) surface.Comment: 22 pages, Latex, 6 postscript figure
Rough Sets Clustering and Markov model for Web Access Prediction
Discovering user access patterns from web access log is increasing the importance of information to build up adaptive web server according to the individual user’s behavior. The variety of user behaviors on accessing information also grows, which has a great impact on the network utilization. In this paper, we present a rough set clustering to cluster web transactions from web access logs and using Markov model for next access prediction. Using this approach, users can effectively mine web log records to discover and predict access patterns. We perform experiments using real web trace logs collected from www.dusit.ac.th servers. In order to improve its prediction ration, the model includes a rough sets scheme in which search similarity measure to compute the similarity between two sequences using upper approximation
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