K-means Type Algorithms Research Articles

A clusterwise nonlinear regression algorithm for interval-valued data

Interval-valued variables are required in data analysis since this type of data represents either the uncertainty existing in an error measurement or the natural variability of the data. Currently, methods and algorithms which aim to manage interval-valued data are very much required. Hence, this paper presents a center and range clusterwise nonlinear regression algorithm for interval-valued data. The proposed algorithm combines a k-means type algorithm with the center and range linear and nonlinear regression methods for interval-valued data, with the aim to identify both the partition of the data and the relevant regression models fitted on the center and range of the intervals simultaneously, one for each cluster. The proposed method is able to automatically select the best pair of center and range (linear and/or nonlinear) functions according to optimization criteria. A simulation study with synthetic data sets with the purpose of assessing the parameter estimation and the prediction performance of the proposed algorithm was undertaken. Finally, applications on real data sets were performed and the prediction accuracy of the proposed method was compared to the linear case. The results obtained showed that the proposed method performed well on both synthetic and real data sets.

Joint Clustering of Users and Resources for Multi-Cell VMIMO-SC-FDMA Uplink Systems

In this paper, we investigate the joint clustering of users and resources based on minimum mean square error-ordered successive interference cancellation (MMSE-OSIC) equalization for multi-cell single carrier-frequency division multiple access (SC-FDMA) uplink systems. To exploit the properties of the MMSE-OSIC equalization, we formulate the dynamic user grouping and resource allocation as a clustering problem, where resources are divided into several clusters and users are assigned into different clusters. Furthermore, we develop a new efficient K-means type algorithm named iterative K-groups clustering algorithm (IKG_OSIC) to solve the optimization problem. By making use of the MMSE-OSIC results, IKG_OSIC algorithm largely reduces the computation complexity of the joint resource allocation problem. The simulation results show that the proposed algorithm achieves good tradeoff between the performance and complexity and has better system throughput than the existing algorithms for virtual multiple-input multiple-output single carrier-frequency division multiple access (VMIMO-SC-FDMA) uplink systems.

Data mining and optimisation issues in the food industry

Data mining applications in the food industry has until now expressed in many ways, on both technical and economic terms. The most important methodologies being used are clustering, classification, feature selection and outlier detection. The techniques commonly used in data mining are artificial neural networks, decision trees, k-means type algorithms, genetic algorithms, nearest neighbour method, and rule induction. Successful case studies of the implementation of these methodologies are fruit and vegetable classification, with special focus on apples, citrus, strawberries, table olives, onions etc. the same rationale is being followed for the evaluation of processed foodstuff. Efficient solutions have been provided for wine classification, based on organoleptic characteristics, fish and meat classification, as well as robotic harvesting. Finally, applications on supply chain management and e-commerce have provided significant solutions on issues of monitoring and evaluation of dynamically changing datasets.

Data mining and optimisation issues in the food industry

Data mining applications in the food industry has until now expressed in many ways, on both technical and economic terms. The most important methodologies being used are clustering, classification, feature selection and outlier detection. The techniques commonly used in data mining are artificial neural networks, decision trees, k-means type algorithms, genetic algorithms, nearest neighbour method, and rule induction. Successful case studies of the implementation of these methodologies are fruit and vegetable classification, with special focus on apples, citrus, strawberries, table olives, onions etc. the same rationale is being followed for the evaluation of processed foodstuff. Efficient solutions have been provided for wine classification, based on organoleptic characteristics, fish and meat classification, as well as robotic harvesting. Finally, applications on supply chain management and e-commerce have provided significant solutions on issues of monitoring and evaluation of dynamically changing datasets.

Data Mining Research Based on Analysis of the Chinese-Foreign Cooperation in Running Schools

Chinese-foreign cooperation in running schools is an important way to promote education reform and development in our country. In this paper, we use the K-Means clustering in data mining method to analyze the data, and data warehouse and the acquisition of knowledge, analyzing the development of sino-foreign cooperation in running schools in China. This article first to 1452 papers in CNKI knowledge base, data warehouse is generated, using the K-Means type algorithm and similarity algorithm, development trend of Chinese-foreign cooperation in running schools, Chinese-foreign cooperation in running schools national distribution, sino-foreign cooperation in running schools, Chinese-foreign cooperation in running schools in domestic distribution of the four aspects are analyzed.

A Robust k-Means Type Algorithm for Soft Subspace Clustering and Its Application to Text Clustering

Soft subspace clustering are effective clustering techniques for high dimensional datasets. Although several soft subspace clustering algorithms have been developed in recently years, its robustness should be further improved. In this work, a novel soft subspace clustering algorithm RSSKM are proposed. It is based on the incorporation of the alternative distance metric into the framework of k-means type algorithm for soft subspace clustering and can automatically calculates the feature weights of each cluster in the clustering process. The properties of RSSKM are also investigated. Experiments on real world text datasets are conducted and the results show that RSSKM outperformed some popular clustering algorithms for text mining, while still maintaining efficiency of the k-means clustering process.

A Novel Decision Cluster Classifier with Nested Agglomerative K-Means

Classification is an important task in machine learning community. The basic assumption of classification is that the objects that are spatially close in data space tend to have the same label, which is quite similar to the objective of clustering, i.e. grouping similar objects into the same cluster. Based on this fact, several researchers have proposed to build classifiers by using the K-means type algorithms to partition the data sequentially, which are named as decision cluster classifiers. While it is well-known that the performance of K-means type algorithms relies on the locations of initial centers and the selections of number of clusters, it is necessary to deal with both issues well such that the clustering performance is good enough to build such classifiers. In this paper, we propose to build decision cluster classifiers by using the agglomerative fuzzy K-means because of its three good properties: (1) the clustering algorithm is insensitive to initial centers; (2) the algorithm can automatically determine the number of clusters combined with cluster validation techniques; (3) the algorithm can control the density level of clusters identified. The experimental results on UCI benchmark data sets have shown the effectiveness of our proposed approach.

Robust seed selection algorithm for k-means type algorithms

Selection of initial seeds greatly affects the quality of the clusters and in k-means type algorithms. Most of the seed selection methods result different results in different independent runs. We propose a single, optimal, outlier insensitive seed selection algorithm for k-means type algorithms as extension to k-means++. The experimental results on synthetic, real and on microarray data sets demonstrated that effectiveness of the new algorithm in producing the clustering results

A k-means type clustering algorithm for subspace clustering of mixed numeric and categorical datasets

Almost all subspace clustering algorithms proposed so far are designed for numeric datasets. In this paper, we present a k-means type clustering algorithm that finds clusters in data subspaces in mixed numeric and categorical datasets. In this method, we compute attributes contribution to different clusters. We propose a new cost function for a k-means type algorithm. One of the advantages of this algorithm is its complexity which is linear with respect to the number of the data points. This algorithm is also useful in describing the cluster formation in terms of attributes contribution to different clusters. The algorithm is tested on various synthetic and real datasets to show its effectiveness. The clustering results are explained by using attributes weights in the clusters. The clustering results are also compared with published results.

Coordination of Cluster Ensembles via Exact Methods

We present a novel optimization-based method for the combination of cluster ensembles for the class of problems with intracluster criteria, such as Minimum-Sum-of-Squares-Clustering (MSSC). We propose a simple and efficient algorithm-called EXAMCE-for this class of problems that is inspired from a Set-Partitioning formulation of the original clustering problem. We prove some theoretical properties of the solutions produced by our algorithm, and in particular that, under general assumptions, though the algorithm recombines solution fragments so as to find the solution of a Set-Covering relaxation of the original formulation, it is guaranteed to find better solutions than the ones in the ensemble. For the MSSC problem in particular, a prototype implementation of our algorithm found a new better solution than the previously best known for 21 of the test instances of the 40-instance TSPLIB benchmark data sets used in [1], [2], and [3], and found a worse-quality solution than the best known only five times. For other published benchmark data sets where the optimal MSSC solution is known, we match them. The algorithm is particularly effective when the number of clusters is large, in which case it is able to escape the local minima found by K-means type algorithms by recombining the solutions in a Set-Covering context. We also establish the stability of the algorithm with extensive computational experiments, by showing that multiple runs of EXAMCE for the same clustering problem instance produce high-quality solutions whose Adjusted Rand Index is consistently above 0.95. Finally, in experiments utilizing external criteria to compute the validity of clustering, EXAMCE is capable of producing high-quality results that are comparable in quality to those of the best known clustering algorithms.

A Text Clustering System based on k-means Type Subspace Clustering and Ontology

This paper presents a text clustering system developed based on a k-means type subspace clustering algorithm to cluster large, high dimensional and sparse text data. In this algorithm, a new step is added in the k-means clustering process to automatically calculate the weights of keywords in each cluster so that the important words of a cluster can be identified by the weight values. For understanding and interpretation of clustering results, a few keywords that can best represent the semantic topic are extracted from each cluster. Two methods are used to extract the representative words. The candidate words are first selected according to their weights calculated by our new algorithm. Then, the candidates are fed to the WordNet to identify the set of noun words and consolidate the synonymy and hyponymy words. Experimental results have shown that the clustering algorithm is superior to the other subspace clustering algorithms, such as PROCLUS and HARP and kmeans type algorithm, e.g., Bisecting-KMeans. Furthermore, the word extraction method is effective in selection of the words to represent the topics of the clusters. Keywords—Subspace Clustering, Text Mining, Feature Weighting, Cluster Interpretation, Ontology

An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data

This paper presents a new k-means type algorithm for clustering high-dimensional objects in sub-spaces. In high-dimensional data, clusters of objects often exist in subspaces rather than in the entire space. For example, in text clustering, clusters of documents of different topics are categorized by different subsets of terms or keywords. The keywords for one cluster may not occur in the documents of other clusters. This is a data sparsity problem faced in clustering high-dimensional data. In the new algorithm, we extend the k-means clustering process to calculate a weight for each dimension in each cluster and use the weight values to identify the subsets of important dimensions that categorize different clusters. This is achieved by including the weight entropy in the objective function that is minimized in the k-means clustering process. An additional step is added to the k-means clustering process to automatically compute the weights of all dimensions in each cluster. The experiments on both synthetic and real data have shown that the new algorithm can generate better clustering results than other subspace clustering algorithms. The new algorithm is also scalable to large data sets.

Automated variable weighting in k-means type clustering

This paper proposes a k-means type clustering algorithm that can automatically calculate variable weights. A new step is introduced to the k-means clustering process to iteratively update variable weights based on the current partition of data and a formula for weight calculation is proposed. The convergency theorem of the new clustering process is given. The variable weights produced by the algorithm measure the importance of variables in clustering and can be used in variable selection in data mining applications where large and complex real data are often involved. Experimental results on both synthetic and real data have shown that the new algorithm outperformed the standard k-means type algorithms in recovering clusters in data.

Multivariate permutation tests for the k-sample problem with clustered data

We investigate the optimal choice of clustering algorithms for multivariate data sets. We make use of algorithms that define partitions by maximal support planes (MSP) of a convex function and have been profoundly investigated by Potzelberger and Strasser (2000). This is a wide range class containing as special cases both the well known k-means algorithm and the Kohonen (1985) algorithm. We compare the quality of the clustering procedures by first applying them to multivariate data sets and then treating a k-sample problem. For computing the test statistics the data points are replaced by their conditional expectations with respect to the MSP-partition. Monte Carlo simulations of power functions for tests that are carried out as multivariate permutation tests show a vital and decisive connection between the optimal choice of the algorithm and the tails of the probability distribution of the data. Especially for distributions with heavy tails the performance of k-means type algorithms totally breaks down.