A Survey of Clustering Ensemble Algorithms

Cluster ensemble has proved to be a good alternative when facing cluster analysis problems. It consists of generating a set of clusterings from the same dataset and combining them into a final clustering. The goal of this combination process is to improve the quality of individual data clusterings. Due to the increasing appearance of new methods, their promising results and the great number of applications, we consider that it is necessary to make a critical analysis of the existing techniques and future projections. This paper presents an overview of clustering ensemble methods that can be very useful for the community of clustering practitioners. The characteristics of several methods are discussed, which may help in the selection of the most appropriate one to solve a problem at hand. We also present a taxonomy of these techniques and illustrate some important applications

Partition Selection Approach for Hierarchical Clustering Based on Clustering Ensemble

Hierarchical clustering algorithms are widely used in many fields of investigation. They provide a hierarchy of partitions of the same dataset. However, in many practical problems, the selection of a representative level (partition) in the hierarchy is needed. The classical approach to do so is by using a cluster validity index to select the best partition according to the criterion imposed by this index. In this paper, we present a new approach based on the clustering ensemble philosophy. The representative level is defined here as the consensus partition in the hierarchy. In the consensus computation process, we take into account the similarity between partitions and information from the evaluation of partitions with different cluster validity indexes. An experimental comparison on several datasets shows the superiority of the proposed approach with respect to the classical approach

Logical Combinatorial Pattern Recognition: A Review

This paper is a survey on Logical Combinatorial Pattern Recognition methods. The intent is to examine its principles, fundamental concepts, tools, and recent advances. It is essential to show by a series of real examples and reasoning; not on the limits of other existing Pattern Recognition theories, but also the perspectives of this pattern recognition branch in the solution of real p. roblems, especially those appearing in soj sciences (medi- cine, geosciences, criminology, and others). Note that there exist real possibilities of ap- plying this theory and its tools in image processing and analysis, data mining, and data fusion problems. This approach was originally introduced in the middle of the sixties in the former Soviet Union by the Russian dcademician Yuri Ivanovich Zhuravlev from the Com- puter Center of the Iussian .dcademy of $ciences

Weighted association based methods for the combination of heterogeneous partitions

Co-association matrix has been a useful tool in many clustering ensemble techniques as a similarity measure between objects. In this paper, we introduce the weighted-association matrix, which is more expressive than the traditional co-association as a similarity measure, in the sense that it integrates information from the set of partitions in the clustering ensemble as well as from the original data of object representations. The weighted-association matrix is the core of the two main contributions of this paper: a natural extension of the well-known evidence accumulation cluster ensemble method by using the weighted association matrix and a kernel based clustering ensemble method that uses a new data representation. These methods are compared with simple clustering algorithms as well as with other clustering ensemble algorithms on several datasets. The obtained results ratify the accuracy of the proposed algorithms

Segmentation ensemble via kernels

Abstract—Clustering ensemble is a promising technique to face data clustering problems. Similarly, the combination of different segmentations to obtain a consensus one could be a powerful tool for addressing image segmentation problems. Such segmentation ensemble algorithms should be able to deal with the possible large image size and should preserve the spatial relation among pixels in the image. In this paper, we formalize the segmentation ensemble problem and introduce a new method to solve it, which is based on the kernel clustering ensemble philosophy. We prove that the Rand index is a kernel function and we use it as similarity measure between segmentations in the proposed algorithm. This algorithm is experimentally evaluated on the Berkeley image database and compared to several state-of-the art clustering ensemble algorithms. The achieved results ratify the accuracy of our proposal

Weighted Partition Consensus via Kernels

The combination of multiple clustering results (clustering ensemble) has emerged as an important procedure to improve the quality of clustering solutions. In this paper we propose a new cluster ensemble method based on kernel functions, which introduces the Partition Relevance Analysis step. This step has the goal of analyzing the set of partition in the cluster ensemble and extract valuable information that can improve the quality of the combination process. Besides, we propose a new similarity measure between partitions proving that it is a kernel function. A new consensus function is introduced using this similarity measure and based on the idea of finding the median partition. Related to this consensus function, some theoretical results that endorse the suitability of our methods are proven. Finally, we conduct a numerical experimentation to show the behavior of our method on several databases by making a comparison with simple clustering algorithms as well as to other cluster ensemble methods

A New Classifier Combination Scheme Using Clustering Ensemble

Combination of multiple classifiers has been shown to increase classification accuracy in many application domains. Besides, the use of cluster analysis techniques in supervised classification tasks has shown that they can enhance the quality of the classification results. This is based on the fact that clusters can provide supplementary constraints that may improve the generalization capability of the classifiers. In this paper we introduce a new classifier combination scheme which is based on the Decision Templates Combiner. The proposed scheme uses the same concept of representing the classifiers decision as a vector in an intermediate feature space and builds more representatives decision templates by using clustering ensembles. An experimental evaluation was carried out on several synthetic and real datasets. The results show that the proposed scheme increases the classification accuracy over the Decision Templates Combiner, and other classical classifier combinations methods

Pronóstico gasopetrolífero en la asociación ofiolítica cubana aplicando modelación matemática

Cuba es uno de los pocos Jugares en el mundo donde se obtiene producción industrial de petró1eo en rocas de la asociación ofiolítica. El primer yacimiento descubierto fue Motembo en 1881 y el más reciente Cantiel en el año 1978. Los afloramientos de ofiolita -en su mayoría de serpentinita- pueden medir desde pocos metros hasta varios kilómetros de extensión y están sobre corridos en dirección de sur a norte, presentado gran tectonismo y metamorfismo superpuesto. Estos yacimientos de poca profundidad presentan grandes débitos de hidrocarburo en su etapa de explotación inicial, el cual migró desde rocas sedimentarias mas profundas principalmente carbonatos. Se detectaban por la perforación de pozos cerca de las manifestaciones de hidrocarburo en la superficie o en las exploraciones hacia objetivos mas profundos, como las secuencias carbonatadas de la Unidad Tectono-Estratigrafica (UTE) Placetas y Camajuani. Se creó una hipótesis genético-dinámica de la formación del cinturón ofiolítico y sus cabalgamientos y un modelo geó1ogo-geoffsico para explicar las características de estas trampas. A partir de este modelo y sobre la base de una novedosa metodóloga de modelación matemática, se aplicó a problemas de reconocimiento de patrones en dominios poco formalizados del conocimiento, se procesó la información correspondiente a las areas en estudio y se dieron respuestas a muchas interrogantes de esta problemática. doi: https://doi.org/10.22201/igeof.00167169p.1994.33.3.118