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Clustering for mixed numerical and categorical attributes has attracted many researchers due to its necessity in many real-world applications. One crucial issue concerned in clustering mixed data is to select an appropriate distance metric for each attribute type. Besides, some current clustering methods are sensitive to the initial solutions and easily trap into a locally optimal solution. Thus, this study proposes a local search genetic algorithm-based possibilistic weighted fuzzy c-means (LSGA-PWFCM) for clustering mixed numerical and categorical data. The possibilistic weighted fuzzy c-means (PWFCM) is firstly proposed in which the object-cluster similarity measure is employed to calculate the distance between two mixed-attribute objects. Besides, each attribute is placed a different important role by calculating its corresponding weight in the PWFCM procedure. Thereafter, GA is used to find a set of optimal parameters and the initial clustering centroids for the PFCM algorithm. To avoid local optimal solution, local search-based variable neighborhoods are embedded in the GA procedure. The proposed LSGA-PWFCM algorithm is compared with other benchmark algorithms based on some public datasets in UCI machine learning repository to evaluate its performance. Two clustering validation indices are used, i.e., clustering accuracy and Rand index. The experimental results show that the proposed LSGA-PWFCM outperforms other algorithms on most of the tested datasets. [ABSTRACT FROM AUTHOR]

With the advance of information technology, many fields have begun using data clustering to reveal data structures and obtain useful information. Most of the existing clustering algorithms are susceptible to outliers and noises as well as the initial solution. The fuzzy c-ordered-means (FCOM) method can handle outlier and noise problems by using Huber's M-estimators and Yager's OWA operator to enhance its robustness. However, the result of the FCOM algorithm is still unstable because its initial centroids are randomly generated. Besides, the attributes' weight also affect the clustering performance. Thus, this study first proposed an intuitionistic fuzzy weighted c-ordered-means (IFWCOM) algorithm that combines intuitionistic fuzzy sets (IFSs), the feature-weighted and FCOM together to improve the clustering result. Moreover, this study proposed a real-coded genetic algorithm-based IFWCOM (GA-IFWCOM) that employs the genetic algorithm to exploit the global optimal solution of the IFWCOM algorithm. Twelve benchmark datasets were used for verification in the experiment. According to the experimental results, the GA-IFWCOM algorithm achieved better clustering accuracy than the other clustering algorithms for most of the datasets. [ABSTRACT FROM AUTHOR]

Due to advances in information technology, data collection is becoming much easier. Clustering is an important technique for exploring data structures used in many fields, such as customer segmentation, image recognition, social science, and so on. However, in real-world applications, there are a lot of noises or outliers which will seriously influence the clustering performance in the dataset. Besides, the clustering results are susceptible to the initial centroids and algorithm parameters. To overcome the influence of outliers on clustering results, this study combines the advantages of probability c-means and fuzzy c-ordered means to propose a fuzzy possibilistic c-ordered means (FPCOM) algorithm. In order to solve the problem of parameters and initial centroids determination, this study employs a sine cosine algorithm (SCA) combined with FPCOM to improve the clustering results. The proposed algorithm is named SCA-FPCOM algorithm. Ten benchmark datasets collected from the UCI machine repository were used to validate the proposed algorithm in terms of adjusted rand index and the Silhouette coefficient. According to the experimental results, the SCA-FPCOM algorithm can obtain better results than other algorithms. [ABSTRACT FROM AUTHOR]

Data analysis becomes more important since rapid technology developments. In data analysis, data clustering is one of the very useful approaches. It can reveal important information hiding inside the dataset by organizing the instances based on their similarity. The objectives of data clustering are maximizing dissimilarity between clusters and minimizing dissimilarity within clusters. In order to construct a good clustering results, many clustering algorithms have been proposed, including the metaheuristic-based clustering algorithms. Recently, a new metaheuristic algorithm named gradient evolution has been proposed. This algorithm shows a good performance on solving the optimization problems. Therefore, this paper employs this GE algorithm for solving the clustering problem. In order to obtain a better clustering result, this paper considers multi-objective clustering instead of single-objective clustering. In this paper, the original GE algorithm is improved so then it is suitable for the multi-objective problem. The proposed modification includes the procedure for vector updating and jumping which involves Pareto rank assignment. In addition, it also employs K-means algorithm to provide the final clustering result. The proposed algorithm is verified using some benchmark datasets. It is also compared with some other multi-objective metaheuristic-based clustering algorithms. The experimental results show that the proposed algorithm can obtain better results than other metaheuristic-based algorithms. [ABSTRACT FROM AUTHOR]

This study proposes an improved differential evolution (DE) algorithm to solve an automatic clustering problem. Automatic clustering is a clustering technique in data mining which can automatically define the best number of clusters, as well as construct those clusters. In order to overcome the weakness of the DE algorithm for automatic clustering, some improvement scenarios are proposed. They include the best solution effect, saturated solution, acceleration and handling downhill scenarios. The best solution effect and acceleration factor are proposed to boost the convergence of the DE algorithm. The saturated solution helps the algorithm maintain the diversity of the population. The handling downhill scenario allows chromosomes to move to a worse solution, since it might later lead to a better solution. The proposed algorithm is evaluated using four well-known datasets. The computational results indicate that the proposed improvements can enhance the DE algorithm's performance. In addition, the proposed method is also applied to cluster customers for a ladies' office wear clothing franchise. The results can be used to plan a marketing strategy for the case company. [ABSTRACT FROM AUTHOR]

This paper intends to enhance the learning performance of radial basis function neural network (RBFnn) using self-organizing map (SOM) neural network (SOMnn). In addition, the particle swarm optimization (PSO) and genetic algorithm (GA) based (PG) algorithm is employed to train RBFnn for function approximation. The proposed mix of SOMnn with PG (MSPG) algorithm combines the automatically clustering ability of SOMnn and the PG algorithm. The simulation results revealed that SOMnn, PSO and GA approaches can be combined ingeniously and redeveloped into a hybrid algorithm which aims for obtaining a more accurate learning performance among relevant algorithms. On the other hand, method evaluation results for four continuous test function experiments and the demand estimation case showed that the MSPG algorithm outperforms other algorithms and the Box-Jenkins models in accuracy. Additionally, the proposed MSPG algorithm is allowed to be embedded into business' enterprise resource planning system in different industries to provide suppliers, resellers or retailers in the supply chain more accurate demand information for evaluation and so to lower the inventory cost. Next, it can be further applied to the intelligent manufacturing system to cope with real situation in the industry to meet the need of customization. [ABSTRACT FROM AUTHOR]

This paper intends to propose an integrated hybrid algorithm for training radial basis function neural network (RBFNN) learning. The proposed integrated of particle swarm and genetic algorithm based optimization (IPGO) algorithm is composed of two approaches based on particle swarm optimization (PSO) and genetic algorithm (GA) for gathering both their virtues to improve the learning performance of RBFNN. The diversity of individuals results in higher chance to search in the direction of global optimal instead of being confined to local optimal particularly in problem with higher complexity. The IPGO algorithm with PSO-based and GA-based approaches has shown promising results in some benchmark problems with three continuous test functions. After proposing the algorithm for these problems with result providing its outperforming performance, this paper supplements a practical application case for the papaya milk sales forecasting to expound the superiority of the IPGO algorithm. In addition, model evaluation results of the case have showed that the IPGO algorithm outperforms other algorithms and auto-regressive moving average (ARMA) models in terms of forecasting accuracy and execution time. [ABSTRACT FROM AUTHOR]

This study proposes a variation immunological system (VIS) algorithm with radial basis function neural network (RBFN) learning for function approximation and the exercise of industrial computer (IC) sales forecasting. The proposed VIS algorithm was applied to the RBFN to execute the learning process for adjusting the network parameters involved. To compare the performance of relevant algorithms, three benchmark problems were used to justify the results of the experiment. With better accuracy in forecasting, the trained RBFN can be practically utilized in the IC sales forecasting exercise to make predictions and could enhance business profit. [ABSTRACT FROM PUBLISHER]

This study proposes a novel artificial immune system (AIS)-based clustering algorithm, which integrates with a K-means (AISK) algorithm for a customer clustering problem. Computational results using Iris, Glass, Wine, and Breast Cancer benchmark datasets indicate that the proposed AIS-based clustering algorithm is more accurate than some particle swarm optimization (PSO)-based clustering algorithms. In addition, the model evaluation results using a daily transaction database provided by a cyberstore also show that the proposed AISK algorithm is superior to PSO-based clustering algorithms. [ABSTRACT FROM AUTHOR]

Particle swarm optimization algorithm (PSOA), which maintains a population of particles, where each particle represents a potential solution to an optimization problem, is a population-based stochastic search process. This study intends to integrate PSOA with K-means to cluster data. It is shown that PSOA can be employed to find the centroids of a user-specified number of clusters. The proposed PSOA is evaluated using four data sets, and compared to the performance of some other PSOA-based methods and K-means method. Computational results show that the proposed method has much potential. A real-world problem for order clustering also illustrates that the proposed method is quite promising. [ABSTRACT FROM AUTHOR]

This study is dedicated to integrating both the clustering method and case-based reasoning (CBR) for developing a diagnostic system in maintenance. The reason for this is that searching similar cases for CBR is time consuming if the case base is fairly large. It is necessary to cluster the cases into some groups, and then perform the search for the most appropriate possible group. A novel approach, the ant colony system clustering algorithm (ASCA), is employed for this purpose. The main advantage of this technique is the reduction in the amount of time used in comparison. A real-life problem for car maintenance has shown evidence of this advantage as well as its precision ability. [ABSTRACT FROM AUTHOR]

An artificial neural network (ANN) model with a bipartite classification scheme is developed to retrieve the chlorophyll‐a concentration (Chl) from sea‐viewing wide field‐of‐view sensor (SeaWiFS) data. Bio‐optical data derived from the SeaWiFS bio‐optical algorithm mini‐workshop (SeaBAM) are used to verify this bipartite artificial neural network (BANN) model. In comparison with SeaWiFS operational algorithms and a general ANN model, the BANN model significantly increases the accuracy of Chl retrieval not only on a log scale but also on a normal scale. The BANN model can significantly improve the accuracy of Chl especially in the high Chl region. The model also performs well in a test with in situ measurements from Taiwan coastal waters. The biases induced by errors in atmospheric correction are also reduced in the coastal water case. [ABSTRACT FROM AUTHOR]

In the proposed model, dynamic inhibitory weights are used to speed up the convergence rate, and a new convergence rule is applied to find all maxima. The hardware Implementation of the proposed model is presented in the study, and simulation results indicate that the proposed model converges much faster than the other networks. [ABSTRACT FROM AUTHOR]

Proposes a polynomial search (PS) algorithm and architecture to solve the motion-estimation problem in video encoding. Block-matching distortion curves of the PS algorithm for CCIR-601 video; Performance comparison between several motion-estimation algorithms; Distortion curves of full search algorithm for a macroblock in different formatted video.

Presents information on a study which developed an algorithm for estimation of a linear-in-parameters model. Set-theoretic estimation with a triangular noise distribution; Simulation results; Conclusions.

Deals with the use of LBG algorithm and proposed block-averaging method to design the domain pools based on a proposed iteration-free fractal image codec. Discussion on the fractal cosing scheme technique used for image compression; Focus of studies on fractal coding techniques; Description on the domain pool designs in conventional fractal coding schemes; Details on the domain pool design and the computer simulation.

Focuses on a study which examined an optical parallel architecture for the random-iteration algorithm, in relation to the decoding of a fractal image, which used iterated-function system (IFS) codes. Conversion of the code value into transmittance in film or a spatial light modulator; Methodology used to conduct the study; Results of the study.