DOCSLIB.ORG

Document Clustering Using Word Clusters Via the Information Bottleneck Method

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Document Clustering Using Word Clusters Via the Information Bottleneck Method Document Clustering using Word Clusters via the Information Bottleneck Method Noam Slonim and Naftali Tishby School of Computer Science and Engineering and The Interdisciplinary Center for Neural Computation The Hebrew University, Jerusalem 91904, Israel email: noamm,tishby @cs.huji.ac.il Abstract have been studied in the context of information retrieval system- s: clustering the documents on the basis of the distributions of We present a novel implementation of the recently introduced in- words that co-occur in the documents, and clustering the words formation bottleneck method for unsupervised document cluster- using the distributions of the documents in which they occur (see ing. Given a joint empirical distribution of words and documents, [28] for in-depth review). In this paper we propose a new method ´Ü Ý µ Ô ,wefirst cluster the words, , so that the obtained word for document clustering, which combines these two approaches clusters, , maximally preserve the information on the documents. under a single information theoretic framework. A recently intro- ´ µ The resulting joint distribution, Ô , contains most of the o- duced principle, termed the information bottleneck method [26] is based on the following simple idea. Given the empirical joint ´ µ Á ´ µ riginal information about the documents, Á , but it is much less sparse and noisy. Using the same procedure distribution of two variables, one variable is compressed so that the mutual information about the other is preserved as much as we then cluster the documents, , so that the information about the word-clusters is preserved. Thus, we first find word-clusters possible. In our case these two variables correspond to the set of that capture most of the mutual information about the set of docu- documents and the set of words. Thus, we may find word-clusters ments, and then find document clusters, that preserve the informa- that capture most of the information about the document corpus, tion about the word clusters. We tested this procedure over sever- or we may extract document clusters that capture most of the in- al document collections based on subsets taken from the standard formation about the words that occur. In this work we combine the two alternatives. We approach this problem using a two stage Æ Û × Ö ÓÙÔ× ¾¼ corpus. The results were assessed by calculating the correlation between the document clusters and the correct la- algorithm. First, we extract word-clusters that capture most of the bels for these documents. Finding from our experiments show that information about the documents. In the second stage we replace this double clustering procedure, which uses the information bot- the original representation of the documents, the co-occurrence tleneck method, yields significantly superior performance com- matrix of documents versus words, by a much more compact rep- pared to other common document distributional clustering algo- resentation based on the co-occurrences of the word-clusters in rithms. Moreover, the double clustering procedure improves all the documents. Using this new document representation, we re- the distributional clustering methods examined here. apply the same clustering procedure to obtain the desired docu- ment clusters. The main advantage of this double-clustering pro- cedure lies in a significant reduction of the inevitable noise of the 1 Introduction original co-occurrence matrix, due to its very high dimension. The reduced matrix, based on the word-clusters, is denser and more ro- Document clustering has long been an important problem in infor- bust, providing a better reflection of the inherent structure of the mation retrieval. Early works suggested improving the efficiency document corpus. and increasing the effectiveness of document retrieval systems by Our main concern is how well this method actually discovers first grouping the documents into clusters (cf. [27] and the refer- this inherent structure. Therefore, instead of evaluating our proce- ences therein). Recently, document clustering has been put for- dure by its effectiveness for an IR system (e.g. [30]), we evaluate ward as an important tool for Web search engines [15] [16] [18] the method on a standard labeled corpus, commonly used to e- [30], navigating and browsing document collections [5] [6] [8] valuate supervised text classification algorithms. In this way we [9] [23] and distributed retrieval [29]. Two types of clustering circumvent the bias caused by the use of a specific IR system. In addition, we view the ‘correct’ labels of the documents as objec- tive knowledge on the inherent structure of the dataset. Specifical- Æ Û × Ö ÓÙÔ× ly we used the ¾¼ dataset, collected by Lang [12], ¼¼¼ ¾¼ which contains about ¾¼ articles evenly distributed over UseNet discussion groups. From this corpus we generated sev- eral subsets and measured clustering performance via the corre- lation between the obtained document clusters and the original newsgroups. We compared several clustering algorithms includ- ing the single-stage information bottleneck algorithm [24], Ward’s method [1] and complete-linkage [28] using the standard tf-idf term weights [20]. We found that double-clustering, using the haps surprisingly, this general problem has an exact optimal for- information bottleneck method, was significantly superior to all mal solution without any assumption about the origin of the join- ´Ü Ý µ the other examined algorithms. In addition, the double-clustering t distribution Ô [26]. This solution is given in terms of Ü ¾ procedure improved performance over other algorithms in all our the three distributions that characterize every cluster : ´Üµ experiments. In other words, clustering the documents by their the prior probability for this cluster, Ô , its membership prob- ´Üܵ words was always inferior to clustering by word-clusters. abilities Ô , and its distribution over the relevance variable, ´Ý ܵ Ô´Üܵ Ô . In general, the membership probabilities, , are ¾ Ü ¾ 2 The Information Bottleneck Method ‘soft’, i.e. every Ü can be assigned to every in some (normalized) probability. The information bottleneck prin- Most clustering algorithms start either from pairwise ‘distances’ ciple determines the distortion measure between the points Ü and È Ô´Ý Üµ Ü Ô´Ý ÜµÔ´Ý Üµ ´Ý ܵ ÐÓ between points (pairwise clustering) or with a distortion measure Ô to be the ÃÄ , the Ý Ô´Ý Üµ between a data point and a class centroid (vector quantization). Kulback-Libeler divergence [4] between the conditional distribu- Given the distance matrix or the distortion measure, the clustering ´Ý ܵ Ô´Ý Üµ tions Ô and . Specifically, the formal solution is given task can be adapted in various ways into an optimization prob- by the following equations which must be solved together, lem consisting of finding a small number of classes with low in- Դܵ Ô´Üܵ ÜÔ ´ ¬ Ô´Ý ÜµÔ´Ý Üµ µ traclass distortion or with high intraclass connectivity. The main ÃÄ ´¬Üµ problem with this approach is in the choice of the distance or dis- È tortion measures. Too often this is an arbitrary choice, sensitive to ½ Ô´ÜÜµÔ´ÜµÔ´Ý Üµ ´Ý ܵ Ô (3) Ü Ô´Üµ the specific representation, which may not accurately reflect the È structure of the various components in the high dimensional data. Դܵ Ô´ÜܵԴܵ In the context of document clustering, a natural measure of Ü ´¬ ܵ similarity of two documents is the similarity between their word where is a normalization factor, and the single positive conditional distributions. Specifically, let be the set of docu- (Lagrange) parameter ¬ determines the “softness” of the classifi- ments and let be the set of words, then for every document we cation. Intuitively, in this procedure the information contained in can define about is ‘squeezed’ through a compact ‘bottleneck’ of clus- Ò´Ý Üµ È ´Ý ܵ Ô (1) ters , that is forced to represent the ‘relevant’ part in w.r.t. to Ò´Ý Üµ Ý ¾ . ´Ý ܵ Ý where Ò is the number of occurrences of the word in the ½ document Ü. Roughly speaking, we would like documents with 2.1 Relation to previous work similar conditional word distributions to belong to the same clus- An important information theoretic based approach to word clus- ter. This formulation of finding a cluster hierarchy of the members tering was carried out by Brown et al [3] who used n-gram mod- of one set (e.g. documents), based on the similarity of their condi- els, and about the same time by Pereira, Tishby and Lee [17] who tional distributions w.r.t the members of another set (e.g. words), introduced an early version of the bottleneck method, using verb- was first introduced in [17] and was called “distributional cluster- object tagging for word sense disambiguation. Hofmann [10] has ing”. recently proposed another procedure, called probabilistic latent The issue of selecting the ‘right’ distance measure between semantic indexing (PLSI) for automated document indexing, mo- distributions remains, however, unresolved in that earlier work. tivated and based upon our earlier work. Using this procedure one Recently, Tishby, Pereira, and Bialek [26] proposed a principled can represent documents (and words) in a low-dimensional ‘latent approach to this problem, which avoids the arbitrary choice of a semantic space’. The latent variables defined in this scheme are distortion or a distance measures. In this new approach, given the somewhat analogous to our variable. However, there are im- ´Ü Ý µ empirical joint distribution of two random variables Ô , one portant differences between these approaches.
Load more

Recommended publications
  • Probabilistic Topic Modelling with Semantic Graph
    Probabilistic Topic Modelling with Semantic Graph B Long Chen( ), Joemon M. Jose, Haitao Yu, Fajie Yuan, and Huaizhi Zhang School of Computing Science, University of Glasgow, Sir Alwyns Building, Glasgow, UK [email protected] Abstract. In this paper we propose a novel framework, topic model with semantic graph (TMSG), which couples topic model with the rich knowledge from DBpedia. To begin with, we extract the disambiguated entities from the document collection using a document entity linking system, i.e., DBpedia Spotlight, from which two types of entity graphs are created from DBpedia to capture local and global contextual knowl- edge, respectively. Given the semantic graph representation of the docu- ments, we propagate the inherent topic-document distribution with the disambiguated entities of the semantic graphs. Experiments conducted on two real-world datasets show that TMSG can significantly outperform the state-of-the-art techniques, namely, author-topic Model (ATM) and topic model with biased propagation (TMBP). Keywords: Topic model · Semantic graph · DBpedia 1 Introduction Topic models, such as Probabilistic Latent Semantic Analysis (PLSA) [7]and Latent Dirichlet Analysis (LDA) [2], have been remarkably successful in ana- lyzing textual content. Specifically, each document in a document collection is represented as random mixtures over latent topics, where each topic is character- ized by a distribution over words. Such a paradigm is widely applied in various areas of text mining. In view of the fact that the information used by these mod- els are limited to document collection itself, some recent progress have been made on incorporating external resources, such as time [8], geographic location [12], and authorship [15], into topic models.
    [Show full text]
  • A Hierarchical Clustering Approach for Dbpedia Based Contextual Information of Tweets
    Journal of Computer Science Original Research Paper A Hierarchical Clustering Approach for DBpedia based Contextual Information of Tweets 1Venkatesha Maravanthe, 2Prasanth Ganesh Rao, 3Anita Kanavalli, 2Deepa Shenoy Punjalkatte and 4Venugopal Kuppanna Rajuk 1Department of Computer Science and Engineering, VTU Research Resource Centre, Belagavi, India 2Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bengaluru, India 3Department of Computer Science and Engineering, Ramaiah Institute of Technology, Bengaluru, India 4Department of Computer Science and Engineering, Bangalore University, Bengaluru, India Article history Abstract: The past decade has seen a tremendous increase in the adoption of Received: 21-01-2020 Social Web leading to the generation of enormous amount of user data every Revised: 12-03-2020 day. The constant stream of tweets with an innate complex sentimental and Accepted: 21-03-2020 contextual nature makes searching for relevant information a herculean task. Multiple applications use Twitter for various domain sensitive and analytical Corresponding Authors: Venkatesha Maravanthe use-cases. This paper proposes a scalable context modeling framework for a Department of Computer set of tweets for finding two forms of metadata termed as primary and Science and Engineering, VTU extended contexts. Further, our work presents a hierarchical clustering Research Resource Centre, approach to find hidden patterns by using generated primary and extended Belagavi, India contexts. Ontologies from DBpedia are used for generating primary contexts Email: [email protected] and subsequently to find relevant extended contexts. DBpedia Spotlight in conjunction with DBpedia Ontology forms the backbone for this proposed model. We consider both twitter trend and stream data to demonstrate the application of these contextual parts of information appropriate in clustering.
    [Show full text]
  • Query-Based Multi-Document Summarization by Clustering of Documents
    Query-based Multi-Document Summarization by Clustering of Documents Naveen Gopal K R Prema Nedungadi Dept. of Computer Science and Engineering Amrita CREATE Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri, Kollam -690525 Amrita School of Engineering [email protected] Amritapuri, Kollam -690525 [email protected] ABSTRACT based Hierarchical Agglomerative clustering, k-means, Clus- Information Retrieval (IR) systems such as search engines tering Gain retrieve a large set of documents, images and videos in re- sponse to a user query. Computational methods such as Au- 1. INTRODUCTION tomatic Text Summarization (ATS) reduce this information Automatic Text Summarization [12] reduces the volume load enabling users to find information quickly without read- of information by creating a summary from one or more ing the original text. The challenges to ATS include both the text documents. The focus of the summary may be ei- time complexity and the accuracy of summarization. Our ther generic, which captures the important semantic con- proposed Information Retrieval system consists of three dif- cepts of the documents, or query-based, which captures the ferent phases: Retrieval phase, Clustering phase and Sum- sub-concepts of the user query and provides personalized marization phase. In the Clustering phase, we extend the and relevant abstracts based on the matching between input Potential-based Hierarchical Agglomerative (PHA) cluster- query and document collection. Currently, summarization ing method to a hybrid PHA-ClusteringGain-K-Means clus- has gained research interest due to the enormous information tering approach. Our studies using the DUC 2002 dataset load generated particularly on the web including large text, show an increase in both the efficiency and accuracy of clus- audio, and video files etc.
    [Show full text]
  • Wordnet-Based Metrics Do Not Seem to Help Document Clustering
    Wordnet-based metrics do not seem to help document clustering Alexandre Passos1 and Jacques Wainer1 Instituto de Computação (IC) Universidade Estadual de Campinas (UNICAMP) Campinas, SP, Brazil Abstract. In most document clustering systems documents are repre- sented as normalized bags of words and clustering is done maximizing cosine similarity between documents in the same cluster. While this representation was found to be very effective at many different types of clustering, it has some intuitive drawbacks. One such drawback is that documents containing words with similar meanings might be considered very different if they use different words to say the same thing. This happens because in a traditional bag of words, all words are assumed to be orthogonal. In this paper we examine many possible ways of using WordNet to mitigate this problem, and find that WordNet does not help clustering if used only as a means of finding word similarity. 1 Introduction Document clustering is now an established technique, being used to improve the performance of information retrieval systems [11], as an aide to machine translation [12], and as a building block of narrative event chain learning [1]. Toolkits such as Weka [3] and NLTK [10] make it easy for anyone to experiment with document clustering and incorporate it into an application. Nonetheless, the most commonly accepted model—a bag of words representation [17], bisecting k-means algorithm [19] maximizing cosine similarity [20], preprocessing the corpus with a stemmer [14], tf-idf weighting [17], and a possible list of stop words [2]—is complex, unintuitive and has some obvious negative consequences.
    [Show full text]
  • Evaluation of Text Clustering Algorithms with N-Gram-Based Document Fingerprints
    Evaluation of Text Clustering Algorithms with N-Gram-Based Document Fingerprints Javier Parapar and Alvaro´ Barreiro IRLab, Computer Science Department University of A Coru~na,Spain fjavierparapar,[email protected] Abstract. This paper presents a new approach designed to reduce the computational load of the existing clustering algorithms by trimming down the documents size using fingerprinting methods. Thorough eval- uation was performed over three different collections and considering four different metrics. The presented approach to document clustering achieved good values of effectiveness with considerable save in memory space and computation time. 1 Introduction and Motivation Document's fingerprint could be defined as an abstraction of the original docu- ment that usually implies a reduction in terms of size. In the other hand data clustering consists on the partition of the input data collection in sets that ideally share common properties. This paper studies the effect of using the documents fingerprints as input to the clustering algorithms to achieve a better computa- tional behaviour. Clustering has a long history in Information Retrieval (IR)[1], but only re- cently Liu and Croft in [2] have demonstrated that cluster-based retrieval can also significantly outperform traditional document-based retrieval effectiveness. Other successful applications of clustering algorithms are: document browsing, search results presentation or document summarisation. Our approach tries to be useful in operational systems where the computing time is a critical point and the use of clustering techniques can significantly improve the quality of the outputs of different tasks as the above exposed. Next, section 2 introduces the background in clustering and document repre- sentation.
    [Show full text]
  • Word Sense Disambiguation in Biomedical Ontologies with Term Co-Occurrence Analysis and Document Clustering
    Int. J. Data Mining and Bioinformatics, Vol. 2, No. 3, 2008 193 Word Sense Disambiguation in biomedical ontologies with term co-occurrence analysis and document clustering Bill Andreopoulos∗, Dimitra Alexopoulou and Michael Schroeder Biotechnological Centre, Technischen Universität Dresden, Germany E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] ∗Corresponding author Abstract: With more and more genomes being sequenced, a lot of effort is devoted to their annotation with terms from controlled vocabularies such as the GeneOntology. Manual annotation based on relevant literature is tedious, but automation of this process is difficult. One particularly challenging problem is word sense disambiguation. Terms such as ‘development’ can refer to developmental biology or to the more general sense. Here, we present two approaches to address this problem by using term co-occurrences and document clustering. To evaluate our method we defined a corpus of 331 documents on development and developmental biology. Term co-occurrence analysis achieves an F -measure of 77%. Additionally, applying document clustering improves precision to 82%. We applied the same approach to disambiguate ‘nucleus’, ‘transport’, and ‘spindle’, and we achieved consistent results. Thus, our method is a viable approach towards the automation of literature-based genome annotation. Keywords: WSD; word sense disambiguation; ontologies; GeneOntology; text mining; annotation; Bayes; clustering; GoPubMed; data mining; bioinformatics. Reference to this paper should be made as follows: Andreopoulos, B., Alexopoulou, D. and Schroeder, M. (2008) ‘Word Sense Disambiguation in biomedical ontologies with term co-occurrence analysis and document clustering’, Int. J. Data Mining and Bioinformatics, Vol.
    [Show full text]
  • A Query Focused Multi Document Automatic Summarization
    PACLIC 24 Proceedings 545 A Query Focused Multi Document Automatic Summarization Pinaki Bhaskar and Sivaji Bandyopadhyay Department of Computer Science & Engineering, Jadavpur University, Kolkata – 700032, India [email protected], [email protected] Abstract. The present paper describes the development of a query focused multi-document automatic summarization. A graph is constructed, where the nodes are sentences of the documents and edge scores reflect the correlation measure between the nodes. The system clusters similar texts having related topical features from the graph using edge scores. Next, query dependent weights for each sentence are added to the edge score of the sentence and accumulated with the corresponding cluster score. Top ranked sentence of each cluster is identified and compressed using a dependency parser. The compressed sentences are included in the output summary. The inter-document cluster is revisited in order until the length of the summary is less than the maximum limit. The summarizer has been tested on the standard TAC 2008 test data sets of the Update Summarization Track. Evaluation of the summarizer yielded accuracy scores of 0.10317 (ROUGE-2) and 0.13998 (ROUGE–SU-4). Keywords: Multi Document Summarizer, Query Focused, Cluster based approach, Parsed and Compressed Sentences, ROUGE Evaluation. 1 Introduction Text Summarization, as the process of identifying the most salient information in a document or set of documents (for multi document summarization) and conveying it in less space, became an active field of research in both Information Retrieval (IR) and Natural Language Processing (NLP) communities. Summarization shares some basic techniques with indexing as both are concerned with identification of the essence of a document.
    [Show full text]
  • Semantic Based Document Clustering Using Lexical Chains
    International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 01 | Jan -2017 www.irjet.net p-ISSN: 2395-0072 SEMANTIC BASED DOCUMENT CLUSTERING USING LEXICAL CHAINS SHABANA AFREEN1, DR. B. SRINIVASU2 1M.tech Scholar, Dept. of Computer Science and Engineering, Stanley College of Engineering and Technology for Women, Telangana-Hyderabad, India. 2Associate Professor, Dept. of Computer Science and Engineering, Stanley College of Engineering and Technology for Women, Telangana -Hyderabad, India. ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract – Traditional clustering algorithms do not organizing large document collections into smaller consider the semantic relationships among documents so that meaningful and manageable groups, which plays an cannot accurately represent cluster of the documents. To important role in information retrieval, browsing and overcome these problems, introducing semantic information comprehension [1]. from ontology such as WordNet has been widely used to improve the quality of text clustering. However, there exist 1.2 Problem Description several challenges such as extracting core semantics from Feature vectors generated using Bow results in very large texts, assigning appropriate description for the generated dimensional vectors. The feature selected is directly clusters and diversity of vocabulary. proportional to dimension. Extract core semantics from texts selecting the feature will In this project we report our attempt towards integrating reduced number of terms which depict high semantic conre WordNet with lexical chains to alleviate these problems. The content. proposed approach exploits the way we can identify the theme The quality of extracted lexical chains is highly depends on of the document based on disambiguated core semantic the quality and quantity of the concepts within a document.
    [Show full text]
  • Latent Semantic Sentence Clustering for Multi-Document Summarization
    UCAM-CL-TR-802 Technical Report ISSN 1476-2986 Number 802 Computer Laboratory Latent semantic sentence clustering for multi-document summarization Johanna Geiß July 2011 15 JJ Thomson Avenue Cambridge CB3 0FD United Kingdom phone +44 1223 763500 http://www.cl.cam.ac.uk/ c 2011 Johanna Geiß This technical report is based on a dissertation submitted April 2011 by the author for the degree of Doctor of Philosophy to the University of Cambridge, St. Edmund’s College. Technical reports published by the University of Cambridge Computer Laboratory are freely available via the Internet: http://www.cl.cam.ac.uk/techreports/ ISSN 1476-2986 Latent semantic sentence clustering for multi-document summarization Johanna Geiß Summary This thesis investigates the applicability of Latent Semantic Analysis (LSA) to sentence clustering for Multi-Document Summarization (MDS). In contrast to more shallow approaches like measuring similarity of sentences by word overlap in a traditional vector space model, LSA takes word usage patterns into account. So far LSA has been successfully applied to different Information Retrieval (IR) tasks like information filtering and document classification (Dumais, 2004). In the course of this research, different parameters essential to sentence clustering using a hierarchical agglomerative clustering algorithm (HAC) in general and in combination with LSA in particular are investigated. These parameters include, inter alia, information about the type of vocabulary, the size of the semantic space and the optimal numbers of dimensions to be used in LSA. These parameters have not previously been studied and evaluated in combination with sentence clustering (chapter 4). This thesis also presents the first gold standard for sentence clustering in MDS.
    [Show full text]
  • Clustering News Articles Using K-Means and N-Grams by Desmond
    CLUSTERING NEWS ARTICLES USING K-MEANS AND N-GRAMS BY DESMOND BALA BISANDU (A00019335) SCHOOL OF IT & COMPUTING, AMERICAN UNIVERSITY OF NIGERIA, YOLA, ADAMAWA STATE, NIGERIA SPRING, 2018 CLUSTERING NEWS ARTICLES USING K-MEANS AND N-GRAMS BY DESMOND BALA BISANDU (A00019335) In partial fulfillment of the requirements for the award of degree of Master of Science (M.Sc.) in Computer Science submitted to the School of IT & Computing, American University of Nigeria, Yola SPRING, 2018 ii DECLARATION I, Desmond Bala BISANDU, declare that the work presented in this thesis entitled ‘Clustering News Articles Using K-means and N-grams’ submitted to the School of IT & Computing, American University of Nigeria, in partial fulfillment of the requirements for the award of the Master of Science (M.Sc.) in Computer Science. I have neither plagiarized nor submitted the same work for the award of any other degree. In case this undertaking is found incorrect, my degree may be withdrawn unconditionally by the University. Date: 16th April, 2018 Desmond Bala Bisandu Place: Yola A00019335 iii CERTIFICATION I certify that the work in this document has not been previously submitted for a degree nor neither has it been submitted as a part of a requirements for a degree except fully acknowledged within this text. ---------------------------- --------------------------- Student Date (Desmond Bala Bisandu) ----------------------------- --------------------------- Supervisor Date (Dr. Rajesh Prasad) ------------------------------ --------------------------- Program Chair Date (Dr. Narasimha Rao Vajjhala) ------------------------------ --------------------------- Graduate Coordinator, SITC Date (Dr. Rajesh Prasad) ------------------------------ --------------------------- Dean, SITC Date (Dr. Mathias Fonkam) iv ACKNOWLEDGEMENT My gratitude goes to the Almighty God for giving me the strength, knowledge, and courage to carry out this thesis successfully.
    [Show full text]
  • A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques
    A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques Mehdi Allahyari Seyedamin Pouriyeh Mehdi Assefi Computer Science Department Computer Science Department Computer Science Department University of Georgia University of Georgia University of Georgia Athens, GA Athens, GA Athens, GA [email protected] [email protected] [email protected] Saied Safaei Elizabeth D. Trippe Juan B. Gutierrez Computer Science Department Institute of Bioinformatics Department of Mathematics University of Georgia University of Georgia Institute of Bioinformatics Athens, GA Athens, GA University of Georgia [email protected] [email protected] Athens, GA [email protected] Krys Kochut Computer Science Department University of Georgia Athens, GA [email protected] ABSTRACT 1 INTRODUCTION The amount of text that is generated every day is increasing dra- Text Mining (TM) field has gained a great deal of attention in recent matically. This tremendous volume of mostly unstructured text years due the tremendous amount of text data, which are created in cannot be simply processed and perceived by computers. There- a variety of forms such as social networks, patient records, health fore, efficient and effective techniques and algorithms are required care insurance data, news outlets, etc. IDC, in a report sponsored to discover useful patterns. Text mining is the task of extracting by EMC, predicts that the data volume will grow to 40 zettabytes1 meaningful information from text, which has gained significant by 2020, leading to a 50-time growth from the beginning of 2010 attentions in recent years. In this paper, we describe several of [52]. the most fundamental text mining tasks and techniques including Text data is a good example of unstructured information, which text pre-processing, classification and clustering.
    [Show full text]
  • Document Clustering Using K-Means and K-Medoids Rakesh Chandra Balabantaray*, Chandrali Sarma**, Monica Jha***
    Article can be accessed online at http://www.publishingindia.com Document Clustering using K-Means and K-Medoids Rakesh Chandra Balabantaray*, Chandrali Sarma**, Monica Jha*** Abstract clustering are big volume, high dimensionality and complex semantics. Our motive in the present paper is to With the huge upsurge of information in day-to-day’s life, extract particular domain of work from a huge collection it has become difficult to assemble relevant information of documents using K-Means and K-Medoids clustering in nick of time. But people, always are in dearth of algorithm and to obtain best clusters which later on time, they need everything quick. Hence clustering can be used for document summarizations. Document was introduced to gather the relevant information in clustering is a more specific technique for document a cluster. There are several algorithms for clustering organization, automatic topic extraction and fast IR1, information out of which in this paper, we accomplish which has been carried out using K-means clustering K-means and K-Medoids clustering algorithm and a algorithm implemented on a tool named WEKA2(Waikato comparison is carried out to find which algorithm is best Environment for Knowledge Analysis) and K-Medoids for clustering. On the best clusters formed, document algorithm is executed on Java NetBeans IDE to obtain summarization is executed based on sentence weight clusters. This allows a collection of various documents to focus on key point of the whole document, which makes it easier for people to ascertain the information from different domains to be segregated into groups of they want and thus read only those documents which similar domain.
    [Show full text]