New approaches like neural networks and fuzzy sets have been used more and more in pattern recognition during recent years. In this article, neural network and fuzzy clustering algorithms are compared to the traditional clustering algorithm.

Recently, the development of technology has enriched form classroom interaction. Exploring characteristics current teaching interaction forms can clarify deficiencies interactions, thereby improving teaching. Based on existing interactive coding system, this paper adopted ITIAS and took with whiteboard, television or mobile terminals as research scene, selected 20 videos cases in environment ob...

Image clustering, defined as the task of finding natural grouping of similar items, is one of the key tasks in computer vision and pattern recognition. This problem has many incarnations in mathematics and applied sciences. Recently many papers have been proposed to tackle clustering solutions using methods in physics, in particular statistical thermodynamics. Some of such method like Deternmin...

Fuzzy C-Means (FCM) clustering is a popular technique used in image segmentation and pattern recognition. However one of the main problems with FCM clustering is the lack of spatial context. That is FCM often fails with irregularly shaped clusters. This can lead to the creation of isolated regions; isolated regions are those regions that are not connected with the main body of the clusters. We ...

Spatial data mining is the extraction of implicit knowledge, spatial relations or other patterns not explicitly stored in spatial database. The focus of this paper is placed on the information derivation of spatial data. Geographical coordinates of hot spots in forest fire regions, which are extracted from the satellite images, are studied and used in the detection of likely fire points. Due to...

The data analysis involves the performance of different tasks, which can be performed by many different techniques and strategies. The data clustering task, an unsupervised pattern recognition process, is the task of assigning a set of objects into groups called clusters so that the objects in the same cluster are more similar to each other than to those in other clusters. This paper describes ...

Applications of fuzzy set theory (FST) to property casualty and life insurance have emerged in the last few years through the work of Lemaire (1990), Cummins and Derrig (1991, 1993) and Ostaszewski (1993). This paper continues that line of research by providing an overview of fuzzy pattern recognition techniques. We utilize them in clustering for risk and claim classification. The classic clust...

Cluster ensemble algorithms have been used in different field like data mining, bioinformatics and pattern recognition. Many of them use label correspondence as a step which can be performed with some accuracy if all the input partitions are generated with same k. Thus these algorithms produce good results if this k is close to the actual number of clusters in the dataset. This puts great restr...

Clustering or cluster analysis [1] is a classical method in unsupervised learning and one of the most used techniques in statistical data analysis. Clustering has a wide range of applications in many areas like pattern recognition, medical diagnostics, data mining, biology, market research and image analysis among others. A cluster is a set of data points that in some sense are similar to each ...

Partitioning a data set and extracting hidden structure arises in diierent application areas of pattern recognition, data analysis and image processing. We formulate data clustering for data characterized by pairwise dissimilarity values as an assignment problem with an objective function to be minimized. An extension to tree{structured clustering is proposed which allows a hierarchical groupin...