27-12-2011, 07:13 PM
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09-01-2012, 02:44 PM
what is the main problem in existing system in this project?
14-07-2012, 10:20 AM
to get infomation about the topic " uml diagrams for clustering with multi viewpoint based similarity measure" related topic refer the link bellow https://seminarproject.net/Thread-cluste...ull-report
18-08-2012, 03:42 PM
Clustering with Multi-Viewpoint based Similarity Measure
Clustering.pdf (Size: 553.02 KB / Downloads: 136) Abstract All clustering methods have to assume some cluster relationship among the data objects that they are applied on. Similarity between a pair of objects can be defined either explicitly or implicitly. In this paper, we introduce a novel multi-viewpoint based similarity measure and two related clustering methods. The major difference between a traditional dissimilarity/similarity measure and ours is that the former uses only a single viewpoint, which is the origin, while the latter utilizes many different viewpoints, which are objects assumed to not be in the same cluster with the two objects being measured. Using multiple viewpoints, more informative assessment of similarity could be achieved. Theoretical analysis and empirical study are conducted to support this claim. Two criterion functions for document clustering are proposed based on this new measure. INTRODUCTION Clustering is one of the most interesting and important topics in data mining. The aim of clustering is to find intrinsic structures in data, and organize them into meaningful subgroups for further study and analysis. There have been many clustering algorithms published every year. They can be proposed for very distinct research fields, and developed using totally different techniques and approaches. Nevertheless, according to a recent study [1], more than half a century after it was introduced, the simple algorithm k-means still remains as one of the top 10 data mining algorithms nowadays. It is the most frequently used partitional clustering algorithm in practice. Another recent scientific discussion [2] states that k-means is the favourite algorithm that practitioners in the related fields choose to use. Needless to mention, k-means has more than a few basic drawbacks, such as sensitiveness to initialization and to cluster size, and its performance can be worse than other state-of-the-art algorithms in many domains. RELATED WORK First of all, Table 1 summarizes the basic notations that will be used extensively throughout this paper to represent documents and related concepts. Each document in a corpus corresponds to an m-dimensional vector d, where m is the total number of terms that the document corpus has. Document vectors are often subjected to some weighting schemes, such as the standard Term Frequency-Inverse Document Frequency (TF-IDF), and normalized to have unit length. The principle definition of clustering is to arrange data objects into separate clusters such that the intra-cluster similarity as well as the inter-cluster dissimilarity is maximized. The problem formulation itself implies that some forms of measurement are needed to determine such similarity or dissimilarity. There are many state-of-theart clustering approaches that do not employ any specific form of measurement, for instance, probabilistic modelbased method [9], non-negative matrix factorization [10], information theoretic co-clustering [11] and so on. In this paper, though, we primarily focus on methods that indeed do utilize a specific measure. PERFORMANCE EVALUATION OF MVSC To verify the advantages of our proposed methods, we evaluate their performance in experiments on document data. The objective of this section is to compare MVSCIR and MVSC-IV with the existing algorithms that also use specific similarity measures and criterion functions for document clustering. The similarity measures to be compared includes Euclidean distance, cosine similarity and extended Jaccard coefficient. Document collections The data corpora that we used for experiments consist of twenty benchmark document datasets. Besides reuters7 and k1b, which have been described in details earlier, we included another eighteen text collections so that the examination of the clustering methods is more thorough and exhaustive. Similar to k1b, these datasets are provided together with CLUTO by the toolkit’s authors [19]. They had been used for experimental testing in previous papers, and their source and origin had also been described in details [30], [31]. Table 2 summarizes their characteristics. The corpora present a diversity of size, number of classes and class balance. They were all preprocessed by standard procedures. CONCLUSIONS AND FUTURE WORK In this paper, we propose a Multi-Viewpoint based Similarity measuring method, named MVS. Theoretical analysis and empirical examples show that MVS is potentially more suitable for text documents than the popular cosine similarity. Based on MVS, two criterion functions, IR and IV , and their respective clustering algorithms, MVSC-IR and MVSC-IV , have been introduced. Compared with other state-of-the-art clustering methods that use different types of similarity measure.
22-09-2012, 04:26 PM
Clustering with Multi-Viewpoint based Similarity Measure
clustering.pdf (Size: 553.02 KB / Downloads: 54) Abstract All clustering methods have to assume some cluster relationship among the data objects that they are applied on. Similarity between a pair of objects can be defined either explicitly or implicitly. In this paper, we introduce a novel multi-viewpoint based similarity measure and two related clustering methods. The major difference between a traditional dissimilarity/similarity measure and ours is that the former uses only a single viewpoint, which is the origin, while the latter utilizes many different viewpoints, which are objects assumed to not be in the same cluster with the two objects being measured. Using multiple viewpoints, more informative assessment of similarity could be achieved. Theoretical analysis and empirical study are conducted to support this claim. Two criterion functions for document clustering are proposed based on this new measure. We compare them with several well-known clustering algorithms that use other popular similarity measures on various document collections to verify the advantages of our proposal. INTRODUCTION Clustering is one of the most interesting and important topics in data mining. The aim of clustering is to find intrinsic structures in data, and organize them into meaningful subgroups for further study and analysis. There have been many clustering algorithms published every year. They can be proposed for very distinct research fields, and developed using totally different techniques and approaches. Nevertheless, according to a recent study [1], more than half a century after it was introduced, the simple algorithm k-means still remains as one of the top 10 data mining algorithms nowadays. It is the most frequently used partitional clustering algorithm in practice. Another recent scientific discussion [2] states that k-means is the favourite algorithm that practitioners in the related fields choose to use. Needless to mention, k-means has more than a few basic drawbacks, such as sensitiveness to initialization and to cluster size, and its performance can be worse than other state-of-the-art algorithms in many domains. In spite of that, its simplicity, understandability and scalability are the reasons for its tremendous popularity. An algorithm with adequate performance and usability in most of application scenarios could be preferable to one with better performance in some cases but limited usage due to high complexity. While offering reasonable results, kmeans is fast and easy to combine with other methods in larger systems. RELATED WORK First of all, Table 1 summarizes the basic notations that will be used extensively throughout this paper to represent documents and related concepts. Each document in a corpus corresponds to an m-dimensional vector d, where m is the total number of terms that the document corpus has. Document vectors are often subjected to some weighting schemes, such as the standard Term Frequency-Inverse Document Frequency (TF-IDF), and normalized to have unit length. The principle definition of clustering is to arrange data objects into separate clusters such that the intra-cluster similarity as well as the inter-cluster dissimilarity is maximized. The problem formulation itself implies that some forms of measurement are needed to determine such similarity or dissimilarity. There are many state-of-theart clustering approaches that do not employ any specific form of measurement, for instance, probabilistic modelbased method [9], non-negative matrix factorization [10], information theoretic co-clustering [11] and so on. In this paper, though, we primarily focus on methods that indeed do utilize a specific measure. PERFORMANCE EVALUATION OF MVSC To verify the advantages of our proposed methods, we evaluate their performance in experiments on document data. The objective of this section is to compare MVSCIR and MVSC-IV with the existing algorithms that also use specific similarity measures and criterion functions for document clustering. The similarity measures to be compared includes Euclidean distance, cosine similarity and extended Jaccard coefficient. Document collections The data corpora that we used for experiments consist of twenty benchmark document datasets. Besides reuters7 and k1b, which have been described in details earlier, we included another eighteen text collections so that the examination of the clustering methods is more thorough and exhaustive. Similar to k1b, these datasets are provided together with CLUTO by the toolkit’s authors [19]. They had been used for experimental testing in previous papers, and their source and origin had also been described in details [30], [31]. Table 2 summarizes their characteristics. The corpora present a diversity of size, number of classes and class balance. They were all preprocessed by standard procedures, including stopword removal, stemming CONCLUSIONS AND FUTURE WORK In this paper, we propose a Multi-Viewpoint based Similarity measuring method, named MVS. Theoretical analysis and empirical examples show that MVS is potentially more suitable for text documents than the popular cosine similarity. Based on MVS, two criterion functions, IR and IV , and their respective clustering algorithms, MVSC-IR and MVSC-IV , have been introduced. Compared with other state-of-the-art clustering methods that use different types of similarity measure
03-10-2012, 10:43 AM
to get information about the topic "clustering with multi viewpoint based similarity measure" full report ppt and related topic refer the link bellow
https://seminarproject.net/Thread-cluste...ull-report http://project-seminars.com/attachment.php?aid=32089 https://seminarproject.net/Thread-cluste...#pid113033
04-10-2012, 04:47 PM
Meaning about Former and alternative forms what you mentioned in your paper.please can you explain me more which helps to understand more.[email=visaraji[at]gmail.com, saran.saranyap[at]gmail.com]Clustering with Mult-viewpoint based Similarity Meaures[/email]
Thank you, S.Visalakshi
23-01-2013, 01:58 PM
Clustering With Multi-Viewpoint Based Similarity Measure Project
1Clustering With Multi.doc (Size: 37.5 KB / Downloads: 24) ABSTRACT: All clustering methods have to assume some cluster relationship among the data objects that they are applied on. Similarity between a pair of objects can be defined either explicitly or implicitly. In this paper, we introduce a novel multi-viewpoint based similarity measure and two related clustering methods. The major difference between a traditional dissimilarity/similarity measure and ours is that the former uses only a single viewpoint, which is the origin, while the latter utilizes many different viewpoints, which are objects assumed to not be in the same cluster with the two objects being measured. Using multiple viewpoints, more informative assessment of similarity could be achieved. Theoretical analysis and empirical study are conducted to support this claim. Two criterion functions for document clustering are proposed based on this new measure. We compare them with several well-known clustering algorithms that use other popular similarity measures on various document collections to verify the advantages of our proposal. EXISTING SYSTEMS • Clustering is one of the most interesting and important topics in data mining. The aim of clustering is to find intrinsic structures in data, and organize them into meaningful subgroups for further study and analysis. There have been many clustering algorithms published every year. • Existing Systems greedily picks the next frequent item set which represent the next cluster to minimize the overlapping between the documents that contain both the item set and some remaining item sets. • In other words, the clustering result depends on the order of picking up the item sets, which in turns depends on the greedy heuristic. This method does not follow a sequential order of selecting clusters. Instead, we assign documents to the best cluster. PROPOSED SYSTEM • The main work is to develop a novel hierarchal algorithm for document clustering which provides maximum efficiency and performance. • It is particularly focused in studying and making use of cluster overlapping phenomenon to design cluster merging criteria. Proposing a new way to compute the overlap rate in order to improve time efficiency and “the veracity” is mainly concentrated. Based on the Hierarchical Clustering Method, the usage of Expectation-Maximization (EM) algorithm in the Gaussian Mixture Model to count the parameters and make the two sub-clusters combined when their overlap is the largest is narrated. • Experiments in both public data and document clustering data show that this approach can improve the efficiency of clustering and save computing time. |
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