Fuzzy Clustering Of Relational Data Research Articles

A Fuzzy Approach for Multitype Relational Data Clustering

Mining interrelated data among multiple types of objects or entities is important in many real-world applications. Despite extensive study on fuzzy clustering of vector space data, very limited exploration has been made on fuzzy clustering of relational data that involve several object types. In this paper, we propose a new fuzzy clustering approach for multitype relational data (FC-MR). In FC-MR, different types of objects are clustered simultaneously. An object is assigned a large membership with respect to a cluster if its related objects in this cluster have high rankings. In each cluster, an object tends to have a high ranking if its related objects have large memberships in this cluster. The FC-MR approach is formulated to deal with multitype relational data with various structures. The objective function of FC-MR is locally optimized by an efficient iterative algorithm, which updates the fuzzy membership matrix and the ranking matrix of one type at once while keeping those of other types constant. We also discuss the simplified FC-MR for multitype relational data with two special structures, namely, star-structure and extended star-structure. Experimental studies are conducted on benchmark document datasets to illustrate how the proposed approach can be applied flexibly under different scenarios in real-world applications. The experimental results demonstrate the feasibility and effectiveness of the new approach compared with existing ones.

Evolutionary fuzzy clustering of relational data

This paper is concerned with the computational efficiency of fuzzy clustering algorithms when the data set to be clustered is described by a proximity matrix only (relational data) and the number of clusters must be automatically estimated from such data. A fuzzy variant of an evolutionary algorithm for relational clustering is derived and compared against two systematic (pseudo-exhaustive) approaches that can also be used to automatically estimate the number of fuzzy clusters in relational data. An extensive collection of experiments involving 18 artificial and two real data sets is reported and analyzed.

Cell formation using fuzzy relational clustering algorithm

Cellular manufacturing is a useful way to improve overall manufacturing performance. Group technology is used to increase the productivity for manufacturing high quality products and improving the flexibility of manufacturing systems. Cell formation is an important step in group technology. It is used in designing good cellular manufacturing systems. The key step in designing any cellular manufacturing system is the identification of part families and machine groups for the creation of cells that uses the similarities between parts in relation to the machines in their manufacture. There are two basic procedures for cell formation in group technology. One is part-family formation and the other is machine–cell formation. In this paper, we apply a fuzzy relational data clustering algorithm to form part families and machine groups. A real data study shows that the proposed approach performs well based on the grouping efficiency proposed by Chandrasekharan and Rajagopalan.

Local subspace learning by extended fuzzy c-medoids clustering

Linear fuzzy clustering is a technique for extracting linear-shape clusters, in which the fuzzy c-means (FCM)-like iterative procedure is performed with the prototypes of linear varieties, and is also regarded as a local subspace learning method. In fuzzy c-medoids (FCMdd), cluster prototypes are selected from data samples and clustering criteria are calculated by using only mutual distances among samples. Then, it is applicable to relational data clustering. This paper proposes an extended FCMdd approach for linear fuzzy clustering of relational data, which uses multiple representative objects (medoids) for representing prototypes. In the algorithm, new prototype is given by solving a combinatorial optimisation problem for searching medoids and the computational complexity is reduced by searching only from a subset of objects having large membership values. The information summarisation approach can be regarded as a multicluster-type multidimensional scaling for summarising data in multiple low-dimensional feature spaces.

Fuzzy Relational Fixed Point Clustering

The proposed relational fuzzy clustering method, called FRFP (fuzzy relational fixed point), is based on determining a fixed point of a function of the desired membership matrix. The ethod is compared to other relational clustering methods. Simulations show the method to be very effective and less computationally expensive than other fuzzy relational data clustering methods. The membership matrices that are produced by the proposed method are less crisp than those produced by NERFCM and more representative of the proximity matrix that is used as input to the clustering process.

Fuzzy Relational Fixed Point Clustering

The proposed relational fuzzy clustering method, called FRFP (fuzzy relational fixed point), is based on determining a fixed point of a function of the desired membership matrix. The ethod is compared to other relational clustering methods. Simulations show the method to be very effective and less computationally expensive than other fuzzy relational data clustering methods. The membership matrices that are produced by the proposed method are less crisp than those produced by NERFCM and more representative of the proximity matrix that is used as input to the clustering process.

Robust fuzzy clustering of relational data

Popular relational-data clustering algorithms, relational dual of fuzzy c-means (RFCM), non-Euclidean RFCM (NERFCM) (both by Hathaway et al), and FANNY (by Kaufman and Rousseeuw) are examined. A new algorithm, which is a generalization of FANNY, called the fuzzy relational data clustering (FRC) algorithm, is introduced, having an identical objective functional as RFCM. However, the FRC does not have the restriction of RFCM, which is that the relational data is derived from Euclidean distance as the measure of dissimilarity between the objects, and it also does not have limitations of FANNY, including the use of a fixed membership exponent, or a fuzzifier exponent, m. The FRC algorithm is further improved by incorporating the concept of Dave's object data noise clustering (NC) algorithm, done by proposing a concept of noise-dissimilarity. Next, based on the constrained minimization, which includes an inequality constraint for the memberships and corresponding Kuhn-Tucker conditions, a noise resistant, FRC algorithm is derived which works well for all types of non-Euclidean dissimilarity data. Thus it is shown that the extra computations for data expansion (/spl beta/-spread transformation) required by the NERFCM algorithm are not necessary. This new algorithm is called robust non-Euclidean fuzzy relational data clustering (robust-NE-FRC), and its robustness is demonstrated through several numerical examples. Advantages of this new algorithm are: faster convergence, robustness against outliers, and ability to handle all kinds of relational data, including non-Euclidean. The paper also presents a new and better interpretation of the noise-class.

Low-complexity fuzzy relational clustering algorithms for Web mining

This paper presents new algorithms-fuzzy c-medoids (FCMdd) and robust fuzzy c-medoids (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 well-known relational fuzzy c-means 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.