Data clustering is one of the important data mining methods. It is a process of finding classes of a data set with most similarity in the same class and most dissimilarity between different classes. The well known hard clustering algorithm (K -means) and Fuzzy clustering algorithm (FCM) are mostly based on Euclidean distance measure. In this paper, a comparative study of these algorithms with d...

Horizontal collaborative clustering is such a clustering method that carries clustering on one data set describing a pattern set in one feature space with collaborative introducing of outer partition information obtained by clustering on another data set but describing the same pattern set in another feature space. In order to implement the collaborative clustering, horizontal collaborative fuz...

With the development of graphic accelerated hardware and 3D modeling tools, 3D models will be as prevalent as other multimedia data in the future. Thus, effective content-based 3D model retrieval systems are required for emerging needs. Many 3D model retrieval methods have been proposed in recent years. Shape distributions showed superiority over others due to rotation invariance and ease of co...

Value function is usually used to deal with the reinforcement learning problems. In large or even continuous states, function approximation must be used to represent value function. Much of the current work carried out, however, has to design the structure of function approximation in advanced which cannot be adjusted during learning. In this paper, we propose a novel function approximation cal...

The discretization of values plays a critical role in data mining and knowledge discovery. The representation of information through intervals is more concise and easier to understand at certain levels of knowledge than the representation by mean continuous values. In this paper, we propose a method for discretizing continuous attributes by means of a series of fuzzy sets, which constitutes a f...

In real world, customers commonly take relevant attributes into consideration for the selection of products and services. Further, the attribute assessment of a product or service is often presented by a linguistic data sequence. To partition these linguistic data sequences of customers’ assessment on a product or service, a proper clustering method is essential and proposed in this paper. In t...

In this paper we introduce an extension of the lazy learning method called Lazy Induction of Descriptions (LID). This new version is able to deal with fuzzy cases, i.e., cases described by attributes taking continuous values represented as fuzzy sets. LID classifies new cases based on the relevance of the attributes describing them. This relevance is assessed using a distance measure that compa...

A hierarchical fuzzy Bayesian network model for situation assessment is developed in the paper, which includes two layers: the top layer serving as a fusion center, the bottom layer as the continuous data discretization. In this model, Bayesian network (BN) is integrated with the fuzzy theory, which can generalize the continuous variable to fuzzy variable in BN. The fuzzy theory is utilized to ...