Java clustering sentence-level text using a novel fuzzy relational clustering algorithm

•

0 likes•246 views

This paper presents a novel fuzzy clustering algorithm that can cluster sentence-level text into overlapping clusters. Unlike hard clustering methods, fuzzy clustering allows sentences to belong to multiple clusters to different degrees. As sentences can relate to more than one topic, this fuzzy approach is appropriate for sentence clustering. The algorithm uses a graph representation and operates in an expectation-maximization framework to identify semantically related sentences based on their pairwise similarities. It has potential applications in various text mining tasks. Results show it can effectively cluster sentences into overlapping topics.

Education

CLUSTERING SENTENCE-LEVEL TEXT USING A NOVEL FUZZY
RELATIONAL CLUSTERING ALGORITHM
ABSTRACT:
In comparison with hard clustering methods, in which a pattern belongs to a single cluster, fuzzy
clustering algorithms allow patterns to belong to all clusters with differing degrees of
membership. This is important in domains such as sentence clustering, since a sentence is likely
to be related to more than one theme or topic present within a document or set of documents.
However, because most sentence similarity measures do not represent sentences in a common
metric space, conventional fuzzy clustering approaches based on prototypes or mixtures of
Gaussians are generally not applicable to sentence clustering.
This paper presents a novel fuzzy clustering algorithm that operates on relational input data; i.e.,
data in the form of a square matrix of pair wise similarities between data objects. The algorithm
uses a graph representation of the data, and operates in an Expectation-Maximization framework
in which the graph centrality of an object in the graph is interpreted as likelihood. Results of
applying the algorithm to sentence clustering tasks demonstrate that the algorithm is capable of
identifying overlapping clusters of semantically related sentences, and that it is therefore of
potential use in a variety of text mining tasks. We also include results of applying the algorithm
to benchmark data sets in several other domains.
ECWAY TECHNOLOGIES
IEEE PROJECTS & SOFTWARE DEVELOPMENTS
OUR OFFICES @ CHENNAI / TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE
CELL: +91 98949 17187, +91 875487 2111 / 3111 / 4111 / 5111 / 6111
VISIT: www.ecwayprojects.com MAIL TO: ecwaytechnologies@gmail.com

Electrical, Electronics and Computer Engineering, Information Engineering and Technology, Mechanical, Industrial and Manufacturing Engineering, Automation and Mechatronics Engineering, Material and Chemical Engineering, Civil and Architecture Engineering, Biotechnology and Bio Engineering, Environmental Engineering, Petroleum and Mining Engineering, Marine and Agriculture engineering, Aerospace Engineering.

Semantic Ordering Relation- Applied to Agatha Christie Crime Thrillers

ijcoa

In any investigation, logical conclusions play a major part. In the present paper, we investigate the pattern which was widely used by Agatha Christie in her mystery novels and represent those literary elements by the relation called semantically ordering relation. The purpose of introducing this concept is to reduce the vagueness in naturally structured literary presentation by expressing the domain of linguistic variables into the fuzzy hedge based lattice structure. The idea proposed by Ho and Wechler has been applied in our analysis. Lastly, the paper compares the relation between the results obtained by the fuzzy based lattice structure with the projection and Max-Min composition principles.

A Novel Multi- Viewpoint based Similarity Measure for Document Clustering

IJMER

International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education. International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.

Clustering sentence level text using a novel fuzzy relational clustering algo...Ecway Technologies

O NTOLOGY B ASED D OCUMENT C LUSTERING U SING M AP R EDUCE

ijdms

Nowadays, document clustering is considered as a da ta intensive task due to the dramatic, fast increas e in the number of available documents. Nevertheless, th e features that represent those documents are also too large. The most common method for representing docu ments is the vector space model, which represents document features as a bag of words and does not re present semantic relations between words. In this paper we introduce a distributed implementation for the bisecting k-means using MapReduce programming model. The aim behind our proposed implementation i s to solve the problem of clustering intensive data documents. In addition, we propose integrating the WordNet ontology with bisecting k-means in order to utilize the semantic relations between words to enh ance document clustering results. Our presented experimental results show that using lexical catego ries for nouns only enhances internal evaluation measures of document clustering; and decreases the documents features from thousands to tens features. Our experiments were conducted using Amazon ElasticMapReduce to deploy the Bisecting k-means algorithm

Bl24409420IJERA Editor

A STRATEGIC TOPSIS ALGORITHM WITH CORRELATION COEFFICIENT OF INTERVAL VAGUE SETS

ijcoa

This paper aims to develop a new method based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to solve Multiple Attribute Decision Making (MADM) problems for Interval Vague Sets (IVSs). A TOPSIS algorithm is constructed on the basis of the concepts of the relative-closeness coefficient computed from the correlation coefficient of IVSs. This novel method also identifies the positive and negative ideal solutions using the correlation coefficient of IVSs. A numerical illustration explains the proposed algorithms and comparisons are made with various existing methods.

Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Nagar, Chennai-83. Landmark: Next to Kotak Mahendra Bank/Bharath Scans Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry - 9 Landmark: Opp. to Thattanchavady Industrial Estate & Next to VVP Nagar Arch. Landline: (0413) - 4300535 / Mobile: (0)8608600246 / (0)9952649690

PosterSidney Lam

Effective Data Retrieval in XML using TreeMatch Algorithm

IRJET Journal

Path & application(ds)2

GowriLatha1

IEEE PROJECT TOPICS &ABSTRACTS on image processing

aswin tbbc

A survey of xml tree patterns

IEEEFINALYEARPROJECTS

Textmining Retrieval And Clustering

guest0edcaf

Vchunk join an efficient algorithm for edit similarity joins

Vijay Koushik

Similarity join is most important technique to involve many applications such as data integration, record linkage and pattern recognition. Here we introduce new algorithm for similarity join with edit distance constraints. Currently extracting overlapping grams from string and consider only string that share certain gram as candidate. Now we propose extracting non-overlapping substring or chunk from string. Chunk scheme based on tail-restricted chunk boundary dictionary (CBD). This approach integrated existing approach for calculating similarity with several new filters unique to chunk based method. Greedy algorithm automatically select good chunking scheme from given data set. Then show the result our method occupies less space and faster performance to compute the value

Document Classification and Clustering

Ankur Shrivastava

Discovering Novel Information with sentence Level clustering From Multi-docu...

irjes

Specific objective to discover some novel information from a set of documents initially retrieved in response to some query. Clustering sentences level text, effective use and update is still an open research issue, especially in domain of text mining. Since most existing system uses pattern belong to a single cluster. But here we can use patterns belongs to all cluster with different degree of membership. Since sentences of those documents we would expect at least one of the clusters to be closely related to the concepts described by the query term. This paper presents a Novel Fuzzy Clustering Algorithm that operates on relational input data (i.e. data in the form of square matrix of pair wise similarities between data objects).

Clustering sentence level text using a novel fuzzy relational clustering algo...Ecwayt

Clustering sentence level text using a novel fuzzy relational clustering algo...Ecwaytechnoz

Clustering sentence level text using a novel fuzzy relational clustering algo...Ecwayt

Dotnet clustering sentence-level text using a novel fuzzy relational cluster...Ecwayt

Clustering sentence level text using a novel fuzzy relational clustering algo...ecwayprojects

Clustering sentence level text using a novel fuzzy relational clustering algo...Ecway2004

Dotnet clustering sentence-level text using a novel fuzzy relational cluster...Ecwaytech

An Improved Similarity Matching based Clustering Framework for Short and Sent...

IJECEIAES

Text clustering plays a key role in navigation and browsing process. For an efficient text clustering, the large amount of information is grouped into meaningful clusters. Multiple text clustering techniques do not address the issues such as, high time and space complexity, inability to understand the relational and contextual attributes of the word, less robustness, risks related to privacy exposure, etc. To address these issues, an efficient text based clustering framework is proposed. The Reuters dataset is chosen as the input dataset. Once the input dataset is preprocessed, the similarity between the words are computed using the cosine similarity. The similarities between the components are compared and the vector data is created. From the vector data the clustering particle is computed. To optimize the clustering results, mutation is applied to the vector data. The performance the proposed text based clustering framework is analyzed using the metrics such as Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR) and Processing time. From the experimental results, it is found that, the proposed text based clustering framework produced optimal MSE, PSNR and processing time when compared to the existing Fuzzy C-Means (FCM) and Pairwise Random Swap (PRS) methods.

Volume 2-issue-6-1969-1973Editor IJARCET

What's hot

Clustering Algorithm with a Novel Similarity Measure

IOSR Journals

Analysis of different similarity measures: Simrank

Abhishek Mungoli

Multiview alignment hashing for

jpstudcorner

PosterSidney Lam

Effective Data Retrieval in XML using TreeMatch Algorithm

IRJET Journal

Path & application(ds)2

GowriLatha1

IEEE PROJECT TOPICS &ABSTRACTS on image processing

aswin tbbc

A survey of xml tree patterns

IEEEFINALYEARPROJECTS

Textmining Retrieval And Clustering

guest0edcaf

Vchunk join an efficient algorithm for edit similarity joins

Vijay Koushik

Document Classification and Clustering

Ankur Shrivastava

What's hot (11)

Clustering Algorithm with a Novel Similarity Measure

Analysis of different similarity measures: Simrank

Multiview alignment hashing for

Poster

Effective Data Retrieval in XML using TreeMatch Algorithm

Path & application(ds)2

IEEE PROJECT TOPICS &ABSTRACTS on image processing

A survey of xml tree patterns

Textmining Retrieval And Clustering

Vchunk join an efficient algorithm for edit similarity joins

Document Classification and Clustering

Similar to Java clustering sentence-level text using a novel fuzzy relational clustering algorithm