Results 1  10
of
36
Lowcomplexity fuzzy relational clustering algorithms for web mining
 IEEE TRANSACTIONS ON FUZZY SYSTEMS
, 2001
"... This paper presents new algorithms—fuzzy cmedoids (FCMdd) and robust fuzzy cmedoids (RFCMdd)—for fuzzy clustering of relational data. The objective functions are based on selecting c representative objects (medoids) from the data set in such a way that the total fuzzy dissimilarity within each clus ..."
Abstract

Cited by 47 (2 self)
 Add to MetaCart
This paper presents new algorithms—fuzzy cmedoids (FCMdd) and robust fuzzy cmedoids (RFCMdd)—for fuzzy clustering of relational data. The objective functions are based on selecting c representative objects (medoids) from the data set in such a way that the total fuzzy dissimilarity within each cluster is minimized. A comparison of FCMdd with the wellknown relational fuzzy cmeans algorithm (RFCM) shows that FCMdd is more efficient. We present several applications of these algorithms to Web mining, including Web document clustering, snippet clustering, and Web access log analysis.
A Fuzzy Relative of the kMedoids Algorithm with Application to Web Document and Snippet Clustering
 Snippet Clustering, in Proc. IEEE Intl. Conf. Fuzzy Systems  FUZZIEEE99, Korea
, 1999
"... This paper presents new algorithms (Fuzzy cMedoids FCMdd and Fuzzy c Trimmed Medoids or FCTMdd) for fuzzy clustering of relational data. The objective functions are based on selecting c representative objects (medoids) from the data set in such a way that the total dissimilarity within each cluster ..."
Abstract

Cited by 29 (2 self)
 Add to MetaCart
(Show Context)
This paper presents new algorithms (Fuzzy cMedoids FCMdd and Fuzzy c Trimmed Medoids or FCTMdd) for fuzzy clustering of relational data. The objective functions are based on selecting c representative objects (medoids) from the data set in such a way that the total dissimilarity within each cluster is minimized. A comparison of FCMdd with the Relational Fuzzy cMeans algorithm (RFCM) shows that FCMdd is much faster. We present examples of applications of these algorithms to Web document and snippet clustering. 1.Introduction Object data refers to the the situation where the objects to be clustered are represented by vectors x i 2 ! p . Relational data refers to the situation where we have only numerical values representing the degrees to which pairs of objects in the data set are related. Algorithms that generate partitions of relational data are usually referred to as relational (or sometimes pairwise) clustering algorithms. Relational clustering is more general in the sense tha...
An Introduction to Symbolic Data Analysis and the Sodas Software
 Journal of Symbolic Data Analysis
, 2003
"... ..."
Automatic Web User Profiling and Personalization Using Robust Fuzzy Relational Clustering
, 2002
"... The proliferation of information on the world wide Web has made the personalization of this information space a necessity. Personalization of content returned from a Web site is a desired feature that can enhance server performance improve system design, and lead to wise marketing decisions in elect ..."
Abstract

Cited by 15 (4 self)
 Add to MetaCart
The proliferation of information on the world wide Web has made the personalization of this information space a necessity. Personalization of content returned from a Web site is a desired feature that can enhance server performance improve system design, and lead to wise marketing decisions in electronic commerce. Mining typical user profiles from the vast amount of historical data stored in access logs is an important component of Web personalization. In the absence of a priori knowledge, unsupervised or clustering methods seem to be ideally suited to categorize the usage behavior of Web surfers. In this chapter, we present a framework for mining typical user profiles from server acces logs based on robust fuzzy relational clustering. As a byproduct of the clustering process that generates robust profiles, associations between different URL addresses on a given site can easily be inferred. In general, the URLs that are present in the same profile tend to be visited together in the same session or form a large itemset. Finally, we present a personalization system that uses previously mined profiles to automatically generate a Web page containing URLs the user might be interested in. Our personalization approach is based on profiles computed from the prior traversal patterns of the users on the website and do not involve providing any declarative private information or the user to log in.
Multidimensional Scaling of IntervalValued Dissimilarity Data
 Pattern Recognition Letters
, 2000
"... Multidimensional scaling is a wellknown technique for representing measurements of dissimilarity among objects as points in a pdimensional space. In this paper, this method is extended to the case where dissimilarities are only known to lie within certain intervals. Each object is then no longer r ..."
Abstract

Cited by 5 (1 self)
 Add to MetaCart
(Show Context)
Multidimensional scaling is a wellknown technique for representing measurements of dissimilarity among objects as points in a pdimensional space. In this paper, this method is extended to the case where dissimilarities are only known to lie within certain intervals. Each object is then no longer represented as point, but as a region of R p , in such a way that the minimum and maximum distances between two regions approximate the lower and upper bounds of the dissimilarity interval between the two objects. Experiments with real data demonstrate the ability of this method to represent both the structure and the precision of dissimilarity measurements. Keywords: Multidimensional scaling, Intervalvalued data, Exploratory data analysis, Data visualization. 1
Multidimensional scaling of intervalvalued dissimilarity data
 Pattern Recognition Letters
, 2000
"... Multidimensional scaling is a wellknown technique for representing measurements of dissimilarity among objects as points in a pdimensional space. In this paper, this method is extended to the case where dissimilarities are only known to lie within certain intervals. Each object is then no longer r ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
(Show Context)
Multidimensional scaling is a wellknown technique for representing measurements of dissimilarity among objects as points in a pdimensional space. In this paper, this method is extended to the case where dissimilarities are only known to lie within certain intervals. Each object is then no longer represented as point, but as a region of R p,insuchaway that the minimum and maximum distances between two regions approximate the lower and upper bounds of the dissimilarity interval between the two objects. Experiments with real data demonstrate the ability of this method to represent both the structure and the precision of dissimilarity measurements. Keywords: Multidimensional scaling, Intervalvalued data, Exploratory data analysis, Data visualization.
Measuring the Similarity for Heterogenous Data: An Ordered ProbabilityBased Approach
"... Abstract. In this paper we propose a solution to the similarity measuring for heterogenous data. The key idea is to consider the similarity of a given attributevalue pair as the probability of picking randomly a value pair that is less similar than or equally similar in terms of order relations de ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
(Show Context)
Abstract. In this paper we propose a solution to the similarity measuring for heterogenous data. The key idea is to consider the similarity of a given attributevalue pair as the probability of picking randomly a value pair that is less similar than or equally similar in terms of order relations defined appropriately for data types. Similarities of attribute value pairs are then integrated into similarities between data objects using a statistical method. Applying our method in combination with distancebased clustering to real data shows the merit of our proposed method. Key words: data mining, similarity measures, heterogenous data, order relations, probability integration. 1
Minimization Subproblems and Heuristics for an Applied Clustering Problem
, 2001
"... A practical problem that requires the classification of a set of points of R^n using a criterion not sensitive to bounded outliers is studied in this paper. A fixedpoint (kmeans) algorithm is defined that uses an arbitrary distance function. Finite convergence is proved. A robust distance defined ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
A practical problem that requires the classification of a set of points of R^n using a criterion not sensitive to bounded outliers is studied in this paper. A fixedpoint (kmeans) algorithm is defined that uses an arbitrary distance function. Finite convergence is proved. A robust distance defined by Boente, Fraiman and Yohai is selected for applications. Smooth approximations of this distance are defined and suitable heuristics are introduced to enhance the probability of finding global optimizers. A reallife example is presented and commented.
An Adaptive Color Texture Segmentation Using Similarity Measure of Symbolic Object Approach
"... Texture segmentation is the process of partitioning an image into regions with different textures containing similar group of pixels. Texture is an important spatial feature, useful for identifying object or region of interest. In texture analysis the foremost task is to extract texture features, wh ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
Texture segmentation is the process of partitioning an image into regions with different textures containing similar group of pixels. Texture is an important spatial feature, useful for identifying object or region of interest. In texture analysis the foremost task is to extract texture features, which efficiently embody the information about the textural characteristics of the image. This can be used for the segmentation of different textured images. This paper presents a new approach for color texture segmentation using Haralick’s features extracted from color cooccurrence matrices. The originality of this approach is to select the most discriminating color texture features extracted from the color cooccurrence. Symbolic Object Approach is used for achieving texture segmentation.
Attribute Analysis in Biomedical Text Classification
"... Text Classification tasks are becoming increasingly popular in the field of Information Access. Being approached as Machine Learning problems, the definition of suitable attributes for each task is approached in an adhoc way. We believe that a more principled framework is required, and we present i ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
Text Classification tasks are becoming increasingly popular in the field of Information Access. Being approached as Machine Learning problems, the definition of suitable attributes for each task is approached in an adhoc way. We believe that a more principled framework is required, and we present initial insights on attribute engineering for Text Classification, along with a software library that allows experiment definition and fast prototyping of classification systems. The library is currently being used and evaluated in Information Access projects in the biomedical domain.