1. V.Leela Prasad, B.Simmi Cintre / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 4, July-August 2012, pp.409-420 Analysis of Novel Multi-Viewpoint Similarity Measures V.Leela Prasad*, B.Simmi Cintre** *(Student Scholar (MTech), Department of CSE, Adams Engineering College, JNTUH, Khammam,AP-507115, India) ** (Associate professor, Department of CSE, Adams Engineering College, JNTUH, Khammam, AP-507115, India)Abstract— algorithms in many domains. In spite of that, its All clustering methods have to assume simplicity, understandability and scalability are thesome cluster relationship among the data objects reasons for its tremendous popularity. An algorithm withthat they are applied on. Similarity between a pair adequate performance and usability in most of applicationof objects can be defined either explicitly or scenarios could be preferable to one with betterimplicitly. In this paper, we introduce a novel performance in some cases but limited usage due to highmulti-viewpoint based similarity measure and two complexity. While offering reasonable results, k-means isrelated clustering methods. The major difference fast and easy to combine with other methods in largerbetween a traditional dissimilarity/similarity systems.measure and ours is that the former uses only a Our study of similarity of clustering wassingle viewpoint, which is the origin, while the initially motivated by a research on automated textlatter utilizes many different viewpoints, which are categorization of foreign language texts, as explainedobjects assumed to not be in the same cluster with below. As the amount of digital documents has beenthe two objects being measured. Using multiple increasing dramatically over the years as the Internetviewpoints, more informative assessment of grows, information management, search, and retrieval,similarity could be achieved. Theoretical analysis etc., have become practically important problems.and empirical study are conducted to support this Developing methods to organize large amounts ofclaim. Two criterion functions for document unstructured text documents into a smaller number ofclustering are proposed based on this new measure. meaningful clusters would be very helpful as documentWe compare them with several well-known clustering is vital to such tasks as indexing, filtering,clustering algorithms that use other popular automated metadata generation, word sensesimilarity measures on various document disambiguation, population of hierarchical catalogues ofcollections to verify the advantages of our proposal. web resources and, in general, any application requiring document organization .Key Terms—Document clustering, text mining, Document clustering is also useful for topicssimilarity measure, Clustering methods such as Gene Ontology in biomedicine where hierarchical catalogues are needed. To deal with the large amounts ofI. INTRODUCTION data, machine learning approaches have been applied to Clustering is one of the most interesting perform Automated Text Clustering (ATC). Given anand important topics in data mining. The aim of unlabeled dataset, this ATC system builds clusters ofclustering is to find intrinsic structures in data, and documents that are hopefully similar to clusteringorganize them into meaningful subgroups for further (classification, categorization, or labeling) performed bystudy and analysis. There have been many clustering human experts. To identify a suitable tool and algorithmalgorithms published every year. They can be proposed for clustering that produces the best clustering solutions, itfor very distinct research fields, and developed using becomes necessary to have a method for comparing thetotally different techniques and approaches. results of different clustering algorithms. ThoughNevertheless, according to a recent study [1], more than considerable work has been done in designing clusteringhalf a century after it was introduced, the simple algorithms, not much research has been done onalgorithm k-means still remains as one of the top 10 formulating a measure for the similarity of two differentdata mining algorithms nowadays. It is the most clustering algorithms. Thus, the main goal of this paper isfrequently used partitional clustering algorithm in to: First, propose an algorithm for performing similaritypractice. Another recent scientific discussion [2] states analysis among different clustering algorithms; second,that k-means is the favourite algorithm that practitioners apply the algorithm to calculate similarity of various pairsin the related fields choose to use. Needless to mention, of clustering methods applied to a Portuguese corpus andk-means has more than a few basic drawbacks, such as the Iris dataset; finally, to cross validate the results ofsensitiveness to initialization and to cluster size, and its similarity analysis with the Euclidean (centroids) distancesperformance can be worse than other state-of-the-art 409 | P a g e
2. V.Leela Prasad, B.Simmi Cintre / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 4, July-August 2012, pp.409-420and Pearson correlation coefficient, using the same kdatasets. Possible applications are discussed. min∑ ∑ || di – Cr ||2 r=1 di Є Sr The work in this paper is motivated by However, for data in a sparse and high-investigations from the above and similar research dimensional space, such as that in document clustering,findings. It appears to us that the nature of similarity cosine similarity is more widely used. It is also a popularmeasure plays a very important role in the success or similarity score in text mining and information retrievalfailure of a clustering method. Our first objective is to [12]. Particularly, similarity of two document vectors d iderive a novel method for measuring similarity between and dj , Sim(di, dj), is defined as the cosine of the angledata objects in sparse and high-dimensional domain, between them. For unit vectors, this equals to their innerparticularly text documents. From the proposed product:similarity measure, we then formulate new clustering Sim(di,dj ) = cos(di,dj) = ditdjcriterion functions and introduce their respective Cosine measure is used in a variant of k-meansclustering algorithms, which are fast and scalable like called spherical k-means [3]. While k-means aims tok-means, but are also capable of providing high-quality minimize Euclidean distance, spherical k-means intendsand consistent performance. to maximize the cosine similarity between documents in a cluster and that cluster’s centroid: The remaining of this paper is organized as k dtiCrfollows. In Section 2, we review related literature on max∑ ∑ ——similarity and clustering of documents. We then present r=1 diЄSr || Cr||our proposal for document similarity measure in Section3.It is followed by two criterion functions for document The major difference between Euclideanclustering and their optimization algorithms in Section distance and cosine similarity, and therefore between k-4. Extensive experiments on real-world benchmark means and spherical k-means, is that the former focusesdatasets are presented and discussed in Sections 5 on vector magnitudes, while the latter emphasizes on.Finally, conclusions and potential future work are vector directions. Besides direct application in sphericalgiven in Section 6. k-means, cosine of document vectors is also widely used in many other document clustering methods as a core2 RELATED WORKS similarity measurement. The min-max cut graph-based Each document in a corpus corresponds to an spectral method is an example [13]. In graph partitioningm-dimensional vector d, where m is the total number approach, document corpus is consider as a graph Gof terms that the document corpus has. Document =(V,E), where each document is a vertex in V and eachvectors are often subjected to some weighting edge in E has a weight equal to the similarity between aschemes, such as the standard Term Frequency- pair of vertices. Min-max cut algorithm tries to minimizeInverse Document Frequency (TF-IDF), and the criterion function.normalized to have unit length. The principle definition of clustering is to In nearest-neighbor graph clustering methods,arrange data objects into separate clusters such that the such as the CLUTO’s graph method above, the conceptintra-cluster similarity as well as the inter-cluster of similarity is somewhat different from the previouslydissimilarity is maximized. The problem formulation discussed methods. Two documents may have a certainitself implies that some forms of measurement are value of cosine similarity, but if neither of them is in theneeded to determine such similarity or dissimilarity. other one’s neighborhood, they have no connectionThere are many state-of-threat clustering approaches between them. In such a case, some context-basedthat do not employ any specific form of measurement, knowledge or relativeness property is already taken intofor instance, probabilistic model based method , non- account when considering similarity. Recently, Ahmadnegative matrix factorization , information theoretic and Dey [21] proposed a method to compute distanceco-clustering and so on. In this paper, though, we between two categorical values of an attribute based onprimarily focus on methods that indeed do utilize a their relationship with all other attributes. Subsequently,specific measure. In the literature, Euclidean distance Ienco et al. [22] introduced a similar context-basedis one of the most popular measures: distance learning method for categorical data. However,Dist (di, dj) = | |di − dj || for a given attribute, they only selected a relevant subset It is used in the traditional k-means of attributes from the whole attribute set to use as thealgorithm. The objective of k-means is to minimize the context for calculating distance between its two values.Euclidean distance between objects of a cluster and More related to text data, there are phrase-based andthat cluster’s centroid: concept-based document similarities. Lakkaraju et al. [23] employed a conceptual tree-similarity measure to 410 | P a g e
3. V.Leela Prasad, B.Simmi Cintre / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 4, July-August 2012, pp.409-420identify similar documents. This method requires research, we include all the measures that were tested inrepresenting documents as concept trees with the help [17] and add another one—the averaged Kullback-of a classifier. For clustering, Chim and Deng [24] Leibler divergence. These five measures are discussedproposed a phrase-based document similarity by below. Different measure not only results in differentcombining suffix tree model and vector space model. final partitions, but also imposes different requirementsThey then used Hierarchical Agglomerative Clustering for the same clustering algorithm, as we will see inalgorithm to perform the clustering task. However, a Section 4.drawback of this approach is the high computationalcomplexity due to the needs of building the suffix tree 3.1 Metricand calculating pairwise similarities explicitly before Not every distance measure is a metric. To qualify as aclustering. There are also measures designed metric, a measure d must satisfy the following fourspecifically for capturing structural similarity among conditions.XML documents [25]. They are essentially different Let x and y be any two objects in a set and d(x, y) be thefrom the document-content measures that are distance between x and y.discussed in this paper. 1. The distance between any two points must be In general, cosine similarity still remains as nonnegative,the most popular measure because of its simple that is, d(x, y) ≥0.interpretation and easy computation, though its 2. The distance between two objects must be zero if andeffectiveness is yet fairly limited. In the following only if the two objects are identical, that is, d(x, y) = 0sections, we propose a novel way to evaluate if and only if x = y.similarity between documents, and consequently 3. Distance must be symmetric, that is, distance fromformulate new criterion functions for document x to y is the same as the distance from y to x, ie.clustering. d(x, y) = d(y, x). 4. The measure must satisfy the triangle inequality,3. SIMILARITY MEASURES whichBefore clustering, a similarity/distance measure must is d(x, z) ≤ d(x, y) + d(y, z).be determined.The measure reflects the degree ofcloseness or separation of the target objects and should 3.2 Euclidean Distancecorrespond to the characteristics that are believed to Euclidean distance is a standard metric for geometricaldistinguish the clusters embedded in the data. In many problems. It is the ordinary distance between two pointscases, these characteristics are dependent on the data and can be easily measured with a ruler in two- or three-or the problem context at hand, and there is no dimensional space. Euclidean distance is widely used inmeasure that is universally best for all kinds of clustering problems, including clustering text. It satisfiesclustering problems. all the above four conditions and therefore is a trueMoreover, choosing an appropriate similarity measure metric. It is also the default distance measure used withis also crucial for cluster analysis, especially for a the K-means algorithm.particular type of clustering algorithms. For example, Measuring distance between text documents, given twothe density-based clustering algorithms, such as documents da and db represented by theirDBScan [4], rely heavily on the similarity → →computation. Density-based clustering finds clusters term vectors ta and tb respectively, the Euclideanas dense areas in the data set, and the density of a distance of the two documents is defined asgiven point is in turn estimated as the closeness of thecorresponding data object to its neighboring objects. →→ mRecalling that closeness is quantified as the DE( ta , tb) =( ∑ | wt,a – wt,b|2)1/2 ,distance/similarity value, we can see that large number t=1of distance/similarity computations are required forfinding dense areas and estimate cluster assignment of where the term set is T = {t1, . . . , tm}. As mentionednew data objects. Therefore, understanding the previously, we use the tfidf value as term weights, that iseffectiveness of different measures is of great wt,a = tfidf(da, t).importance in helping to choose the best one.In general, similarity/distance measures map the 3.3 Cosine Similaritydistance or similarity between the symbolic When documents are represented as term vectors, thedescription of two objects into a single numeric value, similarity of two documents corresponds to thewhich depends on two factors— the properties of the correlation between the vectors. This is quantified as thetwo objects and the measure itself. In order to make cosine of the angle between vectors, that is, the so-calledthe results of this study comparable to previous cosine similarity. Cosine similarity is one of the most 411 | P a g e
4. V.Leela Prasad, B.Simmi Cintre / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 4, July-August 2012, pp.409-420popular similarity measure applied to text documents, The Jaccard coefficient is a similarity measure andsuch as in numerous information retrieval applications ranges between 0 and 1. It is 1 when the[21] and clustering too [9]. → → ta = tb and 0 when ta and tb are disjoint, where 1 means the two objects are the same and 0 means they are → → completely different. The corresponding distanceGiven two documents ta and tb , their cosine measure is DJ = 1 − SIMJ and we will use DJ instead insimilarity is subsequent experiments. → → →→ ta . tb 3.5 Averaged Kullback-Leibler DivergenceSIMC (ta, tb) = ————— In information theory based clustering, a document is → → considered as a probability distribution of terms. The | ta | × | tb | similarity of two documents is measured as the distance between the two corresponding probability distributions. → → The Kullback- Leibler divergence (KL divergence), alsoWhere ta and tb are m-dimensional vectors over the called the relative entropy, is a widely applied measureterm set T = {t1, . . . , tm}. Each dimension represents for evaluating the differences between two probabilitya term with its weight in the document, which is non- distributions.negative. As a result, the cosine similarity is non-negative and bounded between [0,1]. Given two distributions P and Q, the KL divergence from distribution P to distribution Q is defined asAn important property of the cosine similarity is its Pindependence of document length. For example, DKL(P||Q) = Plog( —)combining two identical copies of a document d to get Qa new pseudo document d1, the cosine similarity In the document scenario, the divergence between twobetween d and d1 is 1, which means that these two distribution of words is:documents are regarded to be identical. Meanwhile, →→ w t,agiven another document l, d and d1 will have the same DKL(ta||tb) = ∑ wt,a × log ( — )similarity value to l, wt,b → → → →that is, sim(td , tl ) = sim( td1 , tl ). In other words, However, unlike the previous measures, the KLdocuments with the same composition but different divergence is not symmetric, ie.totals will be treated identically. Strictly speaking, this DKL(P||Q) ≠ DKL(Q||P). Therefore it is not a true metric.does not satisfy the second condition of a metric, As a result, we use the averaged KL divergence instead,because after all the combination of two copies is a which is defined asdifferent object from the original document. However, DAvgKL(P||Q) = π1DKL(P||M) + π2DKL(Q||M),in practice, when the term vectors are normalized to a P Qunit length such as 1, and in this case the where π1 = —— π2 = —— and M = π1P + π2Q.representation of d and d1 is the same. P+Q , P+Q ,3.4 Jaccard Coefficient The average weighting between two vectors ensuresThe Jaccard coefficient, which is sometimes referred symmetry, that is, the divergence from document i toto as the Tanimoto coefficient, measures similarity as document j is the same as the divergence from documentthe intersection divided by the union of the objects. j to document i. The averaged KL divergence hasFor text document, the Jaccard coefficient compares recently been applied to clustering text documents, suchthe sum weight of shared terms to the sum weight of as in the family of the Information Bottleneck clusteringterms that are present in either of the two document algorithms [18], to good effect.but are not the shared terms. The formal definition is: → → 3.6 novel similarity measure →→ ta . tb The cosine similarity can be expressed in the followingSIMJ (ta, tb) = ———————— form without changing its meaning: → → →→ Sim(di, dj) = cos(di−0, dj−0) = (di−0)t (dj−0) | ta |2× | tb |2 - ta . tb where 0 is vector 0 that represents the origin point. According to this formula, the measure takes 0 as one and only reference point. The similarity between two documents di and dj is determined w.r.t. the angle 412 | P a g e
5. V.Leela Prasad, B.Simmi Cintre / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 4, July-August 2012, pp.409-420between the two points when looking from the origin. these distances, also provides a measure of interclusterTo construct a new concept of similarity, it is possible dissimilarity, given that points di and dj belong to clusterto use more than just one point of reference. We may Sr, whereas dh belongs to another cluster. The overallhave a more accurate assessment of how close or similarity between di and dj is determined by takingdistant a pair of points is, if we look at them from average over all the viewpoints not belonging to clustermany different viewpoints. From a third point dh, the Sr. It is possible to argue that while most of thesedirections and distances to di and dj are indicated viewpoints are useful, there may be some of them givingrespectively by the difference vectors (d i − dh) and (dj misleading information just like it may happen with the− dh). By standing at various reference points dh to origin point. However, given a large enough number ofview di, dj and working on their difference vectors, we viewpoints and their variety, it is reasonable to assumedefine similarity between that the majority of them will be useful. Hence, the effectthe two documents as: of misleading viewpoints is constrained and reduced by 1 the averaging step. It can be seen that this method offersSim(di,dj) = —— ∑ Sim(di – dh, dj - dh) more informative assessment of similarity than the single di,dj Є sr n-nr dhЄssr origin point based similarity measure. 3.7 Analysis and practical examples of MVSAs described by the above equation, similarity of two In this section, we present analytical study to show thatdocuments di and dj - given that they are in the same the proposed MVS could be a very effective similaritycluster - is defined as the average of similarities measure for data clustering. In order to demonstrate itsmeasured relatively from the views of all other advantages, MVS is compared with cosine similaritydocuments outside that cluster. What is interesting is (CS) on how well they reflect the true group structurethat the similarity here is defined in a close relation to in document collections.the clustering problem. A presumption of cluster 1: procedure BUILDMVSMATRIX(A)memberships has been made prior to the measure. The 2: for r ← 1 : c dotwo objects to be measured must be in the same 3: DSSr ← ∑di ¢ Sr dicluster, while the points from where to establish this 4: nSSr ← | S Sr |measurement must be outside of the cluster. We call 5: end forthis proposal the Multi-Viewpoint based Similarity, or 6: for i ← 1 :n doMVS. From this point onwards, we will denote the 7: r ← class of diproposed similarity measure between two document 8: for j ← 1 : n dovectors di and dj by MVS(di, dj | di, dj Є Sr), or 9: if dj Є Sr thenoccasionally MVS(di, dj) for short. DSSrDSSrThe final form of MVS in Eq. depends on particular 10: aij ← dtidj – dti —— - dtj —— + 1formulation of the individual similarities within the nSSr nSSrsum. If the relative similarity is defined by dot-product 11: elseof the difference vectors, we have: DSSrDSSrMVS(di, dj |di, dj Є Sr) 12: aij ← dtidj – dti —— - dtj —— + 1 1 nSSr nSSr= —— ∑ (di,dh)t(dj-dh) 13: end if n-nr dh Є SSr 14: end for 15: end for 1 16: return A={aij }n×n= —— ∑ cos(di-dh,dj-dh)||di – dh || ||dj – dh || 17: end procedure n-nr dh Fig. 1. Procedure: Build MVS similarity matrix.The similarity between two points di and dj insidecluster Sr, viewed from a point dh outside this cluster, From this condition, it is seen that even when dl isis equal to the product of the cosine of the angle considered ―closer‖ to di in terms of CS, i.e.between di and dj looking from dh and the Euclidean cos(di, dj)≤cos(di, dl), dl can still possibly be regarded asdistances from dh to these two points. This definition less similar to di based on MVS if, on the contrary, it isis based on the assumption that dh is not in the same ―closer‖ enough to the outer centroid CSSr than dj is. Thiscluster with di and dj. The smaller the distances is intuitively reasonable, since the ―closer‖ dl is to CSSr ,||di−dh|| and ||dj −dh|| are, the higher the chance that dh the greater the chance it actually belongs to anotheris in fact in the same cluster with di and dj , and the cluster rather than Sr and is, therefore, less similar to di.similarity based on dh should also be small to reflect For this reason, MVS brings to the table an additionalthis potential. Therefore, through useful measure compared with CS. 413 | P a g e
6. V.Leela Prasad, B.Simmi Cintre / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 4, July-August 2012, pp.409-420To further justify the above proposal and analysis, we Two real-world document datasets are used as examplescarried out a validity test for MVS and CS. The in this validity test. The first is reuters7, a subset of thepurpose of this test is to check how much a similarity famous collection, Reuters-21578 Distribution 1.0, ofmeasure coincides with the true class labels. It is based Reuter’s newswire articles1. Reuters-21578 is one of theon one principle: if a similarity measure is appropriate most widely used test collection for text categorization.for the clustering problem, for any of a document in In our validity test, we selected 2,500 documents fromthe corpus, the documents that are closest to it based the largest 7 categories: ―acq‖, ―crude‖, ―interest‖,on this measure should be in the same cluster with ―earn‖, ―money-fx‖, ―ship‖ and ―trade‖ to form reuters7.it.The validity test is designed as following. For each Some of the documents may appear in more than onetype of similarity measure, a similarity matrix A category. The second dataset is k1b, a collection of 2,340={aij}n×n is created. For CS, this is simple, as aij = dti web pages from the Yahoo! subject hierarchy, includingdj .The procedure for building MVS matrix is 6 topics: ―health‖, ―entertainment‖, ―sport‖, ―politics‖,described in Fig. 1. Firstly, the outer composite w.r.t. ―tech‖ and ―business‖. It was created from a past studyeach class is determined. Then, for each row ai of A, i in information retrieval called WebAce [26], and is now= 1, . . . , n, if the pair of documents d i and dj, j = 1, . . . available with the CLUTO toolkit [19]., n are in the same class, aij is calculated as in line 10, The two datasets were preprocessed by stop-wordFig. 1. Otherwise, dj is assumed to be in d i’s class, and removal and stemming. Moreover, we removed wordsaij is calculated as in line 12, Fig. 1. After matrix A is that appear in less than two documents or more thanformed, the procedure in Fig. 2 is used to get its 99.5% of the total number of documents. Finally, thevalidity score. For each document d i corresponding to documents were weighted by TF-IDF and normalized torow ai of A, we select qr documents closest to di. The unit vectors.value of qr is chosen relatively as percentage of the For example, with k1b dataset at percentage = 1.0, MVS’size of the class r that contains di, where percentage ∈ validity score is 0.80, while that of CS is only 0.67. This(0, 1]. Then, validity w.r.t. di is calculated by the indicates that, on average, when we pick up anyfraction of these qr documents having the same class document and consider its neighborhood of size equal tolabel with di, as in line 12, Fig. 2. The final validity is its true class size, only 67% of that document’sdetermined by averaging neighbors based on CS actually belong to its class. If Require: 0 < percentage ≤ 1 based on MVS, the number of valid neighbors increases1: procedure GETVALIDITY(validity,A, percentage) to 80%. The validity test has illustrated the potential2: for r ← 1 : c do advantage of the new multi-viewpoint based similarity3: qr ← [percentage × nr] measure compared to the cosine measure.4: if qr = 0 then5: qr ← 16: end if 4.MULTI-VIEWPOINT BASED7: end for CLUSTERING8: for i ← 1 : n do Having defined our similarity measure, we now9: {aiv[1], . . . , aiv[n] } ←Sort {ai1, . . . , ain} formulate our clustering criterion functions. The first10: s.t. aiv[1] ≥ aiv[2] ≥ . . . ≥ aiv[n] function, called IR, is the cluster size-weighted sum of {v[1], . . . , v[n]} ← permute {1, . . . , n} average pairwise similarities of documents in the same11: r ← class of di cluster. Firstly, let us express this sum in a general form12: validity(di) ←|{dv[1], . . . , dv[qr]} ∩ Sr| by function F: —————————— k qr F= ∑ nr [ 1 / n2r ∑ Sim(di,dj) ]13: end for di,djЄ Sr14: validity ←∑ni←1 validity(di) We would like to transform this objective function into —————— some suitable form such that it could facilitate the n optimization procedure to be performed in a simple, fast15: return validity and effective way. Let us use a parameter α called the16: end procedure regulating factor, which has some constant value (α Є [0, 1]), and let λr = n αr in Eq. the final form of ourFig. 2. Procedure: Get validity score criterion function IR is:over all the rows of A, as in line 14, Fig. 2. It is clearthat validity score is bounded within 0 and 1. Thehigher validity score a similarity measure has, themore suitable it should be for the clustering task. 414 | P a g e
7. V.Leela Prasad, B.Simmi Cintre / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 4, July-August 2012, pp.409-420 completes without any documents being moved to new clusters. Unlike the traditional k-means, this algorithm is a stepwise optimal procedure. While kmeans only k 1 n+nr n+nr updates after all n documents have been reassigned, theIR = ∑ —— [—— || Dr||2 – (—— - 1) DtrD] incremental clustering algorithm updates immediately r=1 nr1-ά n-nr n-nr whenever each document is moved to new cluster. Since every move when happens increases the objectiveIn the empirical study of Section 5.4, it appears that function value, convergence to a local optimum isIR’s performance dependency on the value of α is not guaranteed.very critical. The criterion function yields relatively During the optimization procedure, in each iteration, thegood clustering results for α Є (0, 1). main sources of computational cost are:In the formulation of IR, a cluster quality is measured • Searching for optimum clusters to move individualby the average pairwise similarity between documents documents to: O(nz · k).within that cluster. However, such an approach can • Updating composite vectors as a result of such moves:lead to sensitiveness to the size and tightness of the O(m · k).clusters. With CS, for example, pairwise similarity of where nz is the total number of non-zero entries in alldocuments in a sparse cluster is usually smaller than document vectors. Our clustering approach is partitionalthose in a dense cluster. Though not as clear as with and incremental; therefore, computing similarity matrixCS, it is still is absolutely not needed. If τ denotes the number ofpossible that the same effect may hinder MVS-based iterations the algorithm takes, since nz is often severalclustering if using pairwise similarity. To prevent this, tens times larger than m for document domain, thean alternative approach is to consider similarity computational complexity required for clustering withbetween each document vector and its cluster’s IR and IV is O(nz · k · τ).centroid instead.4.1 Optimization algorithm and complexity 5 PERFORMANCE EVALUATION OF MVSC We denote our clustering framework by To verify the advantages of our proposedMVSC, meaning Clustering with Multi-Viewpoint methods, we evaluate their performance in experimentsbased Similarity. Subsequently, we have MVSC-IR on document data. The objective of this section is toand MVSC-IV , which are MVSC with criterion compare MVSC- IR and MVSC-IV with the existingfunction IR and IV respectively. The main goal is to algorithms that also use specific similarity measures andperform document clustering by optimizing criterion functions for document clustering. TheIR in Eq. and IV in Eq.. For this purpose, the similarity measures to be compared includes Euclideanincremental k-way algorithm [18], [29] - a sequential distance, cosine similarity and extended Jaccardversion of k-means - is employed. Considering that the coefficient.expression of IV depends only on nr and Dr, r = 1, . . . 5.1 Document collections, k, IV can be written in a general form: The data corpora that we used for experiments k consist of twenty benchmark document datasets. BesidesIV =∑Ir (nr,Dr) reuters7 and k1b, which have been described in details r=1 earlier, we included another eighteen text collections so that the examination of the clustering methods is morewhere Ir (nr,Dr) corresponds to the objective value of thorough and exhaustive. Similar to k1b, these datasetscluster r. The same is applied to IR. With this general are provided together with CLUTO by the toolkit’sform, the incremental optimization algorithm, which authors [19]. They had been used for experimentalhas two major steps Initialization and Refinement, is testing in previous papers, and their source and origindescribed in Fig. 5. At Initialization, k arbitrary had also been described in details [30], [31]. Table 2documents are selected to be the seeds from which summarizes their characteristics. The corpora present ainitial partitions are formed. Refinement is a diversity of size, number of classes and class balance.procedure that consists of a number of iterations. They were all preprocessed by standard procedures,During each iteration, the n documents are visited one including stopword removal, stemming, removal of tooby one in a totally random order. Each document is rare as well as too frequent words, TF-IDF weightingchecked if its move to another cluster results in and normalization.improvement of the objective function. If yes, thedocument is moved to the cluster that leads to thehighest improvement. If no clusters are better than thecurrent cluster, the document is not moved. Theclustering process terminates when an iteration 415 | P a g e
8. V.Leela Prasad, B.Simmi Cintre / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 4, July-August 2012, pp.409-420 After a test run, clustering solution is evaluated by comparing the documents’ assigned labels with their true labels provided by the corpus. Three types of external evaluation metric are used to assess clustering TABLE 2 performance. They are the FScore, Normalized Mutual Document datasets Information (NMI) and Accuracy. FScore is an equally weighted combination of the ―precision‖ (P) andData Source c n m Balance ―recall‖(R) values used in information retrieval. Given a clustering solution, FScore is determined as:fbis TREC 17 2,463 2,000 0.075 k nihitech TREC 6 2,301 13,170 0.192 FScore= ∑ — max (Fi,j)k1a WebACE 20 2,340 13,859 0.018 i=1 njk1b WebACE 6 2,340 13,859 0.043la1 TREC 6 3,204 17,273 0.290 where ni denotes the number of documents inla2 TREC 6 3,075 15,211 0.274 class i, nj the number of documents assigned to cluster j,re0 Reuters 13 1,504 2,886 0.018 and ni,j the number of documents shared by class i andre1 Reuters 25 1,657 3,758 0.027 cluster j. From another aspect, NMI measures thetr31 TREC 7 927 10,127 0.006 information the true class partition and the clusterc: # of classes, n: # of documents, m: # of words assignment share.It measures how much knowing aboutBalance= (smallest class size)/(largest class size) the clusters helps us know about the classes. Finally, Accuracy measures the fraction of documents5.2 Experimental setup and evaluation that are correctly labels, assuming a one-to-one correspondence between true classes and assigned To demonstrate how well MVSCs can clusters. Let q denote any possible permutation of indexperform, we compare them with five other clustering set {1, . . . , k}, Accuracy is calculated by:methods on the twenty datasets in Table 2. Insummary, the seven clustering algorithms are: 1 k• MVSC-IR: MVSC using criterion function IR Accuracy = — max ∑ ni,q(i)• MVSC-IV : MVSC using criterion function IV nq i=1• k-means: standard k-means with Euclidean distance• Spkmeans: spherical k-means with CS The best mapping q to determine Accuracy• graphCS: CLUTO’s graph method with CS could be found by the Hungarian algorithm2. For all• graphEJ: CLUTO’s graph with extended Jaccard three metrics, their range is from 0 to 1, and a greater• MMC: Spectral Min-Max Cut algorithm [13] value indicates a better clustering solution. Our MVSC-IR and MVSC-IV programs are 5.3 Resultsimplemented in Java. The regulating factor α in IR is Fig. 6 shows the Accuracy of the sevenalways set at 0.3 during the experiments. We clustering algorithms on the twenty text collections.observed that this is one of the most appropriate Presented in a different way, clustering results based onvalues. A study on MVSC-IR’s performance relative FScore and NMI are reported in Table 3 and Table 4to different α values is presented in a later section. The respectively. For each dataset in a row, the value in boldother algorithms are provided by the C library and underlined is the best result, while the value in boldinterface which is available freely with the CLUTO only is the second to best. It can be observed thattoolkit [19]. For each dataset, cluster number is MVSC-IR and MVSC-IV perform consistently well. Inpredefined equal to the number of true class, i.e. k = c. Fig. 6, 19 out of 20 datasets, except reviews, either bothNone of the above algorithms are guaranteed to find or one of MVSC approaches are in the top twoglobal optimum, and all of them are algorithms. The next consistent performer is Spkmeans.initializationdependent. Hence, for each method, we The other algorithms might work well on certain dataset.performed clustering For example, graphEJ yieldsa few times with randomly initialized values, and outstanding result on classic; graphCS and MMC arechose the best trial in terms of the corresponding good on reviews. But they do not fare very well on theobjective function value. In all the experiments, each rest of the collections.test run consisted of 10 trials. Moreover, the result To have a statistical justification of thereported here on each dataset by a particular clustering clustering performance comparisons, we also carried outmethod is the average of 10 test runs. statistical significance tests. Each of MVSC-IR and 416 | P a g e
9. V.Leela Prasad, B.Simmi Cintre / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 4, July-August 2012, pp.409-420MVSC-IV was paired up with one of the remaining show, the advantage of MVSCIR and MVSC-IV overalgorithms for a paired t-test [32]. Given two paired the other methods is statistically significant. A specialsets X and Y of N measured values, the null case is the graphEJ algorithm. On the one hand, MVSC-hypothesis of the test is that the differences between IR is not significantly better than graphEJ if based onX and Y come from a population with mean 0. The FScore or NMI. On the other hand,alternative hypothesis is that the paired sets differ when MVSC-IR and MVSC-IV are tested obviouslyfrom each other in a significant way. In our better than graphEJ, the p-values can still be consideredexperiment, these tests were done based on the relatively large, although they are smaller than 0.05. Theevaluation values obtained on the twenty datasets. reason is that, as observed before, graphEJ’s results onThe typical 5% significance level was used. For classic dataset are very different from those of the otherexample, considering the pair (MVSC-IR, k-means), algorithms. While interesting, these values can befrom Table 3, it is seen that MVSC-IR dominates k- considered as outliers, and including them in themeans w.r.t. FScore. If the paired t-test returns a p- statistical tests would affect the outcomes greatly.value smaller than 0.05, we reject the null hypothesis Hence, w e also report in Table 5 the tests where classicand say that the dominance is significant. Otherwise, was excluded and only results on the other 19 datasetsthe null hypothesis is true and the comparison is were used.considered insignificant. The outcomes of the paired t-tests are presented in Table 5. As the paired t-tests fbis 1 hitech 0.9 k1a 0.8 k1b 0.7 la1 0.6 Acuracy 0.5 la2 0.4 re0 0.3 re1 0.2 tr31 0.1 reviews 0 wap MVSC-IR k-means graphCS MMC classic la12 new3 417 | P a g e
11. V.Leela Prasad, B.Simmi Cintre / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 4, July-August 2012, pp.409-420 TABLE 4 Clustering results in NMI Data MVSC- MVSC- k-means Spkmeans graphCS graphEJ MMC IR IV fbis .606 .595 .584 .593 .527 .524 .556 hitech .323 .329 .270 .298 .279 .292 .283 k1a .612 .594 .563 .596 .537 .571 .588 k1b .739 .652 .629 .649 .635 .650 .645 la1 .569 .571 .397 .565 .490 .485 .553 la2 .568 .590 .381 .563 .496 .478 .566 re0 .399 .402 .388 .399 .367 .342 .414 re1 .591 .583 .532 .593 .581 .566 .515 tr31 .613 .658 .488 .594 .577 .580 .548 reviews .584 .603 .460 .607 .570 .528 .639 wap .611 .585 .568 .596 .557 .555 .575 classic .574 .644 .579 .577 .558 .928 .543 la12 .574 .584 .378 .568 .496 .482 .558 new3 .621 .622 .578 .626 .580 .580 .577 sports .669 .701 .445 .633 .578 .581 .591 tr11 .712 .674 .660 .671 .634 .594 .666 tr12 .686 .686 .647 .654 .578 .626 .640 tr23 .432 .434 .363 .413 .344 .380 .369 tr45 .734 .733 .640 .748 .726 .713 .667 reuters7 .633 .632 .512 .612 .503 .520 .591Under this circumstance, both MVSC-IR and MVSC-IV outperform graphEJ significantly with good p-values.5.4 Effect of α on MVSC-IR’s performance with α = αi. The same transformation was applied to NMI It has been known that criterion function based and Accuracy to yield relative NMI and relative Accuracypartitional clustering methods can be sensitive to cluster respectively. MVSC-IR performs the best with an αi if itssize and balance. In the formulation of I R , there exists relative measure has a value of 1. Otherwise its relativeparameter α which is called the regulating factor, α Є [0, measure is greater than 1; the larger this value is, the1]. To examine how the determination of α could affect worse MVSC-IR with αi performs in comparison withMVSC-IR’s performance, we evaluated MVSC-IR with other settings of α. Finally, the average relative measuresdifferent values of α from 0 to 1, with 0.1 incremental were calculated over all the datasets to present the overallinterval. The assessment was done based on the performance.clustering results in NMI, FScore and Accuracy, eachaveraged over all the twenty given datasets. Since the 6. CONCLUSIONS AND FUTURE WORKevaluation metrics for different datasets could be very In this paper, we analyses a Multi-Viewpointdifferent from each other, simply taking the average over based Similarity measuring method, named MVS.all the datasets would not be very meaningful. Hence, we Theoretical analysis and empirical examples show thatemployed the method used in [18] to transform the MVS is potentially more suitable for text documents thanmetrics into relative metrics before averaging. On a the popular cosine similarity. Based on MVS, twoparticular document collection S, the relative FScore criterion functions, IR and IV , and their respectivemeasure of MVSC-IR with α = αi is determined as clustering algorithms, MVSC-IR and MVSC-IV , havefollowing been introduced. Compared with other state-of-the-art maxαj{FScore(IR; S, αj)} clustering methods that use different types of similarityrelative FScore (IR; S, αi) = ————————— measure, on a large number of document datasets and FScore(IR; S, αi) under different evaluation metrics, the proposed algorithms show that they could provide significantlywhere αi, αj ∈ {0.0, 0.1, . . . , 1.0}, FScore(IR; S, αi) is improved clustering performance.the FScore result on dataset S obtained by MVSC-IR 419 | P a g e
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