fuzzy clustering in web text mining and its application - ijcsmr

International Journal of Computer Science and Management Research Vol 2 Issue 2 February 2013
ISSN 2278-733X
Abstract
FUZZY CLUSTERING IN WEB TEXT MINING
AND ITS APPLICATION IN IEEE ABSTRACT
CLASSIFICATION
Text Mining, a branch of computer science [1], is the process of
extracting patterns from large data sets by combining methods from
statistics and artificial intelligence with database management. Text
Mining is seen as an increasingly important tool by modern business to
transform data into business intelligence giving an informational
advantage. Web text retrieval refers to text retrieval techniques applied
to Web resources and literature available on the Web. The volume of
published Web research, and therefore the underlying Web knowledge
base, is expanding at an increasing rate. Web text retrieval is a way to
aid researchers in coping with information overload. By discovering
predictive relationships between different pieces of extracted data,
data-mining algorithms can be used to improve the accuracy of
information extraction. However, textual variation due to typos,
abbreviations, and other sources can prevent the productive discovery
and utilization of hard-matching rules. Recent methods of soft
clustering can exploit predictive relationships in textual data. This
paper presents a technique for using soft clustering Text Mining
algorithm to increase the accuracy of Web text extraction.
Experimental results demonstrate that this approach improves text
extraction more effectively that hard keyword matching rules.
Keywords—Fuzzy cluster,TextMining,Webmining,document Clustering
I. INTRODUCTION
Searching the most similar documents to a given one is crucial in
text mining because it is the basic process of many techniques such
as classification or information retrieval. Two of the major issues
that text mining faces are the large amount of documents, millions
in modest cases, and a very high dimensionality of the featured
space. Text documents are usually represented as vectors, where
each dimension corresponds to a term and the value reflects its
Rahul R.Papalkar #1 , Gajendrasingh Chandel *2 ,
#Department of information Tehnology, SSICT, Sehore,(M.P.)India
* Department of information Tehnology, SSICT, Sehore,(M.P.)India
importance in the document. There are many approaches to find the
exact vicinity of an object. However, they suffer the curse of
dimensionality, that is, their performance drastically decreases as
the number of dimensions grows. This problem prevents its
application in text mining. To avoid the curse of dimensionality a
variety of methods based on inexact searching have been pro-posed.
In [1, 2] a probabilistic technique, with a good performance, was
presented. This solution uses some elements of the training set as
pivots or permutants. Basically, the permutants are used to predict
proximity between elements and to reduce the number of real
distance evaluation at query time. Although this method has a good
performance when searching proximities over documents, it
introduces an overload at search time, due to the necessity to
perform a sequential search over permutants [1], or to use an
auxiliary structure to avoid it [2]. This overload increases when the
space dimension or the size of datasets grows. In this paper we
introduce improvements to our access method for indexing
collections of objects representing a very high-dimensional space
presented in [3].For indexing, this method uses a combination of a
graph structure and pivots (used as entry points), and a very fast
search algorithm that uses distance or similarity based measures in
order to obtain the k-nearest neighbors (knn) of novel query objects.
In this paper, we introduce a new fast way to generate the connected
graph and a prune rule to improve searches. Although the time
required generating the index structure grows with the size of
collection of objects used, this process is carried out only once
(offline) and does not affect the query process.
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International Journal of Computer Science and Management Research Vol 2 Issue 2 February 2013
ISSN 2278-733X
II. Literature Survey
processing is to discover important features from raw data. Data
Current Text Mining tools operate on structured data, the kind of
data that resides in large relational databases whereas data in the
multimedia databases are semi-structured or unstructured. Often
compared with text mining, multimedia mining reaches much
higher complexity resulting from: a) The huge volume of data, b)
The variability and heterogeneity of the multimedia data (e.g.
diversity of sensors, time or conditions of acquisition etc) and c)
The multimedia content’s meaning is subjective [6].
Unstructured data
Unstructured data is simply a bit stream. Examples include pixel
level representation for images, video, and audio, and character
level representation for text. Substantial processing and
interpretation are required to extract semantics from unstructured
data [7]. This kind of data is not broken down into smaller logical
structures and is not typically interpreted by the database
Architectures for Multimedia Text Mining
Various architectures are being examined to design and develop a
multimedia Text Mining system. The first architecture includes the
following. Extract data or metadata from the unstructured database.
Store the extracted data in a structured database and apply Text
Miningtools on the structured database [8]. This is illustrated in
figure 2.1.
Figure 2.1 Converting unstructured data to structured data for mining
Figure 2.1 present architecture of applying multimedia mining in
different multimedia types [18]. Data collection is the starting point
of a learning system, as the quality of raw data determines the
overall achievable performance. Then, the goal of data pre-
pre-processing includes data cleaning, normalization,
transformation, feature selection, etc. Learning can be
straightforward, if informative features can be identified at pre-
processing stage. Detailed procedure depends highly on the nature
of raw data and problem’s domain. In some cases, prior knowledge
can be extremely valuable.
Figure 2.2 Multimedia Mining Process
For many systems, this stage is still primarily conducted by domain
experts. The product of data pre-processing is the training set. Given
a training set, a learning model has to be chosen to learn from it. It
must be mentioned that the steps of multimedia mining are often
iterative. The analyst can also jump back and forth between major
tasks in order to improve the results [6].
Figure 2.3 present architecture of applying multimedia mining in different
multimedia types
Figure 2.3 present architecture of applying multimedia mining in
different multimedia types [5]. Here the main stages of the Text
Mining process are (1) domain understanding; (2) data selection; (3)
leaning
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and preprocessing; (4) discovering patterns ;(5)
Rahul R.Papalkar et.al. www.ijcsmr.org

International Journal of Computer Science and Management Research Vol 2 Issue 2 February 2013
ISSN 2278-733X
interpretation; and (6) reporting and using discovered knowledge.
The domain understanding stage requires learning how the results of
data-mining will be used so as to gather all relevant prior
knowledge before mining.
II. METHODOLOGY
This process is done in three steps: information retrieval,
information extraction and text mining. A primary reason for
using Text Mining for web text is to assist in the analysis of
collections of the available web text. Web data is vulnerable to co
linearity because of unknown interrelations. The analysis in this
paper will be augmented by using experiment-based approach.
Before Text Mining algorithms can be used, a target data set will be
assembled. As Text Mining can only uncover patterns already
present in the data, the target dataset must be large enough to
contain these patterns. Pre-process is essential to analyze the
multivariate datasets before clustering or text mining. The target set
is then cleaned. Cleaning removes the observations with noise and
missing data. The web data available with us is first put into a data
warehouse. Before putting the data in the data warehouse the
keyword extraction algorithm is used to find out the keywords from
the full text. This keyword extraction uses partial parser to extract
entity names. This parser uses linguistic rules and statistical
disambiguity to achieve greater precision. The data is then
organized into clusters. Clustering is the task of discovering groups
and structures in the data that are in some way or another "similar",
without using known structures in the data. The clusters will be
created based on the keywords extracted from our web text. These
clusters will be created using fuzzy C mean algorithm. The fuzzy c-
means algorithm is one of the most widely used soft clustering
algorithms. It is a variant of standard k-means algorithm that uses a
soft membership function. Fuzzy C-Means (FCM) clustering
algorithm is one of the most popular fuzzy clustering algorithms.
FCM is based on minimization of the objective function Fm(u, c):
FCM computes the membership uij and the cluster centers cj by:
where m, the fuzzification factor which is a weighting exponent on
each fuzzy membership, is any real number greater than 1, uij is the
degree of membership of xi in the cluster j, xi is the i th of d-
dimensional measured data, cj is the dimension center of the cluster,
d2(xk,ci) is a distance measure between object xk and cluster center
ci, and ||*|| is any norm expressing the similarity between any
measured data and the center.
1. Read input String.
2. Read input search path.
Proposed Algorithm
3. Cluster input string as per C Means Fuzzy Clustering.
4. Read files from selected path with specified extension.
5. Convert selected file into text read format.
6. Search input string cluster into file & store a result into
output cluster directory.
7. Repeat step 5 & 6 until all files are scanned else
8. Stop.
go to step 8.
Here the proposed algorithm is responsible for extracting keywords
present in the full text web article store these keywords in a relation.
Then the actual work of algorithm begins, it starts clustering of
keywords. The algorithm initially picks some keywords that are
extracted. It groups the full text articles based on these keywords. It
means each cluster contains only those articles which contain that
keyword as their part. Then it starts using fuzzy C mean clustering
to combine the clusters together on some similarity measure. Here
we combine two clusters if their similarity measure is greater than
or equal to a specified threshold value. The proposed Algorithm
repeats this process until no more changes are made to the clusters.
Finally the proposed algorithm stores all the clusters in directory.
Here our motive to extract all the full text articles which may be
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International Journal of Computer Science and Management Research Vol 2 Issue 2 February 2013
ISSN 2278-733X
relevant for the user providing the search string, for this out of all extraction that retrieves the relevant text articles more efficiently.
clusters the cluster with largest number of articles is our target.
III. EXPERIMENT RESULT
The experiments were performed on the test application developed
in ASP.Net 3.0. The database contains all the article entries
populated manually from the. The search was performed using the
traditional keyword based search algorithm and compared with the
proposed algorithm. The snapshot for asset of search results is
shown in table 4.1.Given the same data for text extraction, the
proposed algorithm seems to be retrieving approximately 89% more
relevant search results than the keyword based searching.
Input String List of Matching Element found
Keyword Based Search
Fuzzy logic 46 85
Neural Network 43 89
Image mining 49 94
Signal Processing 36 96
120
100
80
60
40
20
0
Proposed
Method
Table 4.1 Comparative study of keyword based search & proposed method
IV. CONCLUSION
List of Matching
Element found
Keyword Based
Search
List of Matching
Element found
Proposed
Method
Extraction of text from web is an essential operation. Given that
there have been many text extraction methods developed; this paper
presents a novel technique that employs keyword based article
clustering to further enhance the text extraction process. The
development of the proposed algorithm is of practical significance;
however it is challenging to design a unified approach of text
The proposed algorithm, using data mining algorithm, seems to
extract the text with contextual completeness in overall, individual
and collective forms, making it able to significantly enhance the text
extraction process from web literature.
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