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Data clustering has been applied in multiple fields such as machine learning, data mining, wireless sensor networks and pattern recognition. One of the most famous clustering approaches is K-means which effectively has been used in many clustering problems, but this algorithm has some problems such as local optimal convergence and initial point sensitivity. Artificial fishes swarm algorithm (AFSA)...
In this paper, we present a novel visual codebook learning approach towards compactness and scale-invariance for dense patch image encoding. Firstly, each image is described as a bag of orderless gridding local patches, each of which is expressed in three scales. Then a unified objective function is proposed to simultaneously enforce the codebook compactness and select the optimal scale for each local...
Artificial fish swarm algorithm (AFSA) is a kind of swarm intelligence algorithms, which has the features of not strict to parameter setting, insensitive to initial values, strong robustness and so on. But the precision can not be very high and artificial fish (AF) often suffers the problem of being trapped in local optima. Especially when the objective function is a multimodel function, this problem...
Content-based image retrieval can be dramatically improved by providing a good initial clustering of visual data. The problem of image clustering is that most current algorithms are not able to identify individual clusters that exist in different feature subspaces. In this paper, we propose a novel approach for subspace clustering based on Ant Colony Optimisation and its learning mechanism. The proposed...
This paper applies the artificial fish swarm algorithm (AFSA) to fuzzy clustering. An improved AFSA with adaptive visual and adaptive step is proposed. AFSA enhances the performance of the fuzzy C-means (FCM) algorithm. A computational experiment shows that AFSA improved FCM out performs both the conventional FCM algorithm and the genetic algorithm (GA) improved FCM.
Visual optimization is a very interesting topic to the application users for many purposes. It enables the user with an interactive platform where, by varying different parameter settings, one can customize a solution. Several attempts of developing generalized evolutionary optimizers are found in literature which work well for function optimization problems only. Solving combinatorial optimization...
In image retrieval and annotation, multi-instance learning has been studied actively. Most of the methods solve the MIL problem in a supervised way. In this paper, we proposed two unsupervised frameworks for clustering multi-instance objects based on expectation maximization (EM) and iterative heuristic optimization respectively. For each framework, we introduced three new algorithms of finding users'...
Clustering is generally done on individual object data representing the entities such as feature vectors or on object relational data incorporated in a proximity matrix.This paper describes another method for finding a fuzzy membership matrix that provides cluster membership values for all the objects based strictly on the proximity matrix. This is a form of relational data clustering. The fuzzy membership...
The proposed relational fuzzy clustering method called FRFP (fuzzy relational fixed point) is not based on minimizing an objective function, as in traditional methods, but rather on determining a fixed point of a function of the desired membership matrix with the proximity matrix as parameter. The proposed method is compared to other relational clustering methods including NERFCM, Rouben's method...
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