handwritten digit recognition can be categorized as a classification problem. probabilistic neural network (pnn) is one of the most effective and useful classifiers, which works based on bayesian rule. in this paper, in order to recognize persian (farsi) handwritten digit recognition, a combination of intelligent clustering method and pnn has been utilized. hoda database, which includes 80000 p...

There are different types of computational approaches like deterministic, random and evolutionary. Evolutionary techniques are also known as nature inspired techniques as these types of techniques have stolen the idea from nature. Genetic algorithm (GA) is one of the most commonly used evolutionary techniques which is used to solve different NP-hard computational problems. GA is based upon the ...

The present study proposes a Customer Behavior Mining Framework on the basis of data mining techniques in a telecom company. This framework takes into account the customers’ behavior patterns and predicts the way they may act in the future. Firstly, clustering technique is used to implement portfolio analysis and previous customers are divided based on socio-demographic features using k</em...

• J. Beringer and E. Hüllermeier. Efficient instance based learning on data streams. Adaptive optimization of the number of clusters in fuzzy clustering. Fuzzy clustering of parallel data streams. Adaptive optimization of the number of clusters in fuzzy clustering.

There are different types of computational approaches like deterministic, random and evolutionary. Evolutionary techniques are also known as nature inspired techniques as these types of techniques have stolen the idea from nature. Genetic algorithm (GA) is one of the most commonly used evolutionary techniques which is used to solve different NP-hard computational problems. GA is based upon the ...

Clustering techniques have obtained adequate results when are applied to data mining problems. Clustering is the process of subdividing an input data set into a desired number of subgroups so that members of the same subgroup are similar and members of different subgroups have diverse properties. Many heuristic algorithms have been applied to the clustering problem, which is known to be NP Hard...

The efficiency of pattern recognition (PR) systems using RBF neural networks to implement their recognition function, depends a lot by the training algorithms of these neural networks and especially, by the specific techniques (e.g., supervised, clustering techniques etc.) used for RBF center positioning. Having as starting point the basic property of genetic algorithms (GA) to represent global...

Clustering is a method which divides data objects into groups based on the information found in data that describes the objects and relationships among them. There are a variety of algorithms have been developed in recent years for solving problems of data clustering. Data clustering algorithms can be either hierarchical or partitioned. Most promising among them are K-means algorithm which is p...

Many clustering methods highly depend on extracted features. In this paper, we propose a joint optimization framework in terms of both feature extraction and discriminative clustering. We utilize graph regularized sparse codes as the features, and formulate sparse coding as the constraint for clustering. Two cost functions are developed based on entropy-minimization and maximum-margin clusterin...