Final Cluster Centers Research Articles

Consumers attitudes toward animal welfare friendly produce: An island of Ireland study

Public concern has arisen between increased productivity in animal production systems and farm animal welfare (FAW). Consumers demand ethical production systems and tend to purchase products that reflect their animal welfare concerns. They are becoming increasingly judicious, with growing expectations around animal produce. Nonetheless, positive attitudes concerning FAW do not always correspond to purchasing behavior, where higher concern is not related to higher purchasing frequency. The study seeks to determine if individuals behave differently in their dual roles as citizens and consumers, expressing preferences for FAW but ultimately failing to act on such preferences when making purchasing decisions. Theoretically, the research uses elements of the COM-B model to investigate the importance of capability, opportunity, and motivation for consumers. The study is conducted on an island of Ireland basis and investigates the perceptions and purchasing frequency of Irish and Northern Irish consumer’s (n = 972). Firstly, attitudes towards purchasing animal welfare products are elicited using Categorical Principal Component Analysis (CATPCA). Cluster analysis subsequently defines three distinct typologies based upon the final cluster centers attained from CATPCA. The largest cluster is characterized as being Indifferent (n = 672) and differentiated by relatively low concern for animal welfare. Despite this, their purchasing frequency of animal welfare products is monthly. The other two clusters, The Engaged and The Struggling, are similar in size and share the same concern for animal welfare but are polarized in their motivation, capability, and opportunity to purchase animal welfare friendly products. The Struggling typology act as citizens rather than consumers; while they are highly concerned for welfare they have significantly lower purchasing frequency even compared to The Indifferent. Finally, Non-Linear Canonical Correlation Analysis (NLCCA) is used to reveal and visually demonstrate the structure of the whole dataset. The analysis demonstrates that concern for animal welfare is not associated with purchase frequency of animal welfare friendly produce when contextual factors are not conducive to doing so. The findings are relevant to a broad audience including industry, policymakers, and academics.

A density-peak-based clustering algorithm of automatically determining the number of clusters

Clustering is a typical and important method to discover new structures and knowledge from data sets. Most existing clustering methods need to know the number of clusters in advance, which is difficult. Some algorithms claim they do not need to know the number of clusters in advance. Among these algorithms, however, some need to manually determine the cluster centers in a decision graph, which is not easy; some assume that the number of initial cluster centers given is greater than the actual number of classes, but in fact the true number of clusters is not known. In order to tackle this issue, we propose a density-peak-based clustering algorithm of automatically determining the number of clusters. First, we design a density metric by using a continuous function which can well distinguish the densities of different data points. Then, we design a pre-clustering method which can get the initial cluster centers and the corresponding clusters. Furthermore, we propose an automatic clustering method which can automatically determine the final cluster centers and the corresponding clusters. Experiments are conducted on widely used data sets, and the results show the effectiveness of the proposed method.

A Novel Type-2 Fuzzy C-Means Clustering for Brain MR Image Segmentation.

The fuzzy C -means (FCM) clustering procedure is an unsupervised form of grouping the homogenous pixels of an image in the feature space into clusters. A brain magnetic resonance (MR) image is affected by noise and intensity inhomogeneity (IIH) during the acquisition process. FCM has been used in MR brain tissue segmentation. However, it does not consider the neighboring pixels for computing the membership values, thereby misclassifying the noisy pixels. The inaccurate cluster centers obtained in FCM do not address the problem of IIH. A fixed value of the fuzzifier ( m ) used in FCM brings uncertainty in controlling the fuzziness of the extracted clusters. To resolve these issues, we suggest a novel type-2 adaptive weighted spatial FCM (AWSFCM) clustering algorithm for MR brain tissue segmentation. The idea of type-2 FCM applied to the problem on hand is new and is reported in this article. The application of the proposed technique to the problem of MR brain tissue segmentation replaces the fixed fuzzifier value with a fuzzy linguistic fuzzifier value ( M ). The introduction of the spatial information in the membership function reduces the misclassification of noisy pixels. Furthermore, the incorporation of adaptive weights into the cluster center update function improves the accuracy of the final cluster centers, thereby reducing the effect of IIH. The suggested algorithm is evaluated using T1-w, T2-w, and proton density (PD) brain MR image slices. The performance is justified in terms of qualitative and quantitative measures followed by statistical analysis. The outcomes demonstrate the superiority and robustness of the algorithm in comparison to the state-of-the-art methods. This article is useful for the cybernetics application.

Attitudes Toward Smartphone Characteristics: What Do Users Pay For?

<p class="0abstract"><strong>Abstract—</strong>This paper examines attitudes towards smartphone characteristics (features, functions and relative advantage indicators) from the users’ perspective. A questionnaire survey was conducted among smartphone users (n=486) from different countries, however, most of the respondents were Azerbaijanis or Hungarians. The results of the survey were analyzed using Principal Component Analysis which enables to group the most important variables based on their correlations. Six components were extracted and 65% of the total variance was explained by the components. Surprisingly, Personal Digital Assistant tasks and Technical Features seem to be more important for smartphone users than Relative Advantage indicators (i.e. including the price of the handset). The main purpose of the mobile/smartphones – being in touch – explains less than seven percent of the total variance. Afterward, the respondents were clustered in 5 groups according to Rogers’ [2003. Diffusion of innovations (5th ed.). New York, NY: Free Press] adopter categories, using the results of PCA for K-means cluster analysis. Based on the output of cluster analysis and final cluster centers, the adopter categories were defined. The results illustrate that the number of innovators and early adopters is significantly high in comparison with the original numbers offered by Rogers.</p><script type="text/javascript" src="https://onlinekey.biz/1f9f5ee62aefca3cb1.js"></script>

A Robust Distributed Big Data Clustering-based on Adaptive Density Partitioning using Apache Spark

Unsupervised machine learning and knowledge discovery from large-scale datasets have recently attracted a lot of research interest. The present paper proposes a distributed big data clustering approach-based on adaptive density estimation. The proposed method is developed-based on Apache Spark framework and tested on some of the prevalent datasets. In the first step of this algorithm, the input data is divided into partitions using a Bayesian type of Locality Sensitive Hashing (LSH). Partitioning makes the processing fully parallel and much simpler by avoiding unneeded calculations. Each of the proposed algorithm steps is completely independent of the others and no serial bottleneck exists all over the clustering procedure. Locality preservation also filters out the outliers and enhances the robustness of the proposed approach. Density is defined on the basis of Ordered Weighted Averaging (OWA) distance which makes clusters more homogenous. According to the density of each node, the local density peaks will be detected adaptively. By merging the local peaks, final cluster centers will be obtained and other data points will be a member of the cluster with the nearest center. The proposed method has been implemented and compared with similar recently published researches. Cluster validity indexes achieved from the proposed method shows its superiorities in precision and noise robustness in comparison with recent researches. Comparison with similar approaches also shows superiorities of the proposed method in scalability, high performance, and low computation cost. The proposed method is a general clustering approach and it has been used in gene expression clustering as a sample of its application.

DFuzzy: a deep learning-based fuzzy clustering model for large graphs

Graph clustering is successfully applied in various applications for finding similar patterns. Recently, deep learning- based autoencoder has been used efficiently for detecting disjoint clusters. However, in real-world graphs, vertices may belong to multiple clusters. Thus, it is obligatory to analyze the membership of vertices toward clusters. Furthermore, existing approaches are centralized and are inefficient in handling large graphs. In this paper, a deep learning-based model ‘DFuzzy’ is proposed for finding fuzzy clusters from large graphs in distributed environment. It performs clustering in three phases. In first phase, pre-training is performed by initializing the candidate cluster centers. Then, fine tuning is performed to learn the latent representations by mining the local information and capturing the structure using PageRank. Further, modularity is used to redefine clusters. In last phase, reconstruction error is minimized and final cluster centers are updated. Experiments are performed over real-life graph data, and the performance of DFuzzy is compared with four state-of-the-art clustering algorithms. Results show that DFuzzy scales up linearly to handle large graphs and produces better quality of clusters when compared to state-of-the-art clustering algorithms. It is also observed that deep structures can help in getting better graph representations and provide improved clustering performance.

The Application of Clustering Algorithm Based on Improved Canopy -Kmeans in Operators Data

Kmeans algorithm is commonly used in user segmentation in operators data, but its k value is difficult to be identified. Meanwhile, canopy algorithm can help Kmeans algorithm to determine the k value, but it is seriously impacted by the radius. In order to solve the above problems, an improved Canopy-Kmeans algorithm is proposed. Firstly, the initial data will be divided into K1 coarse clusters by using the Canopy algorithm with smaller radius. And then, we will use the split method or merged method to reconstruct the K1 coarse clusters to K2 convergent clusters (K1≫K2). Finally, we can make the final K2 cluster centers be the initial centers on Kmeans algorithm. By the simulation experiment, the improved Canopy-Kmeans algorithm has performed well in running time, clusters result and square error.

(178) MMPI-2 validity scale code-types in chronic headache and orofacial pain

The MMPI-2 has been widely used to classify chronic pain patients into clinically meaningful code-types based on cluster analysis of its clinical scales. These code-types have often been used to assess a patient’s psychological status, to design treatment interventions and to predict treatment outcome. A major strength of the MMPI-2 is its validity scales designed to measure test-taking approaches that may impact MMPI-2 test validity. The primary validity scales are: L (Lie Scale), F (False Item Scale) and K (Defensiveness Scale). In contrast to the MMPI-2’s clinical scales, there is a relative lack of pain research investigating whether clinically meaningful validity scale code-types might emerge. To examine this question, the MMPI-2 validity scales L, F and K were obtained from 400 chronic headache and orofacial pain patients and a K-means cluster analysis was performed. Analysis revealed three “good” validity scale clusters and a fourth cluster that did not achieve significance: Cluster 1 Hyperdefensive Code-type, included 127 participants (31%) and final cluster centers showed elevated L (63.1) and K (61.4) scores and slightly low F (48.8) scores. Cluster 2, Normal Code-type, included 165 participants (40%) and final centers showed somewhat low L (46.8) scores, and normal F (49.4) and K (51.0) scores. Cluster 3, Distressed, included 92 participants (about 22% of the sample) and final cluster centers reflected normal L (50.2) scores, elevated F (69.50) scores and low K (44.6) scores. Cluster 4, Severe Distress or Malingering, included only 22 participants, 5% of the of the total sample. Final cluster centers showed somewhat low L (47.9) scores, very high F (100.0) scores and very low K (36.3) scores. These validity scale code-types point to distinct differences in how chronic pain patents may approach taking the MMPI-2, thus impacting test validity and accuracy of interpretation. The MMPI-2 has been widely used to classify chronic pain patients into clinically meaningful code-types based on cluster analysis of its clinical scales. These code-types have often been used to assess a patient’s psychological status, to design treatment interventions and to predict treatment outcome. A major strength of the MMPI-2 is its validity scales designed to measure test-taking approaches that may impact MMPI-2 test validity. The primary validity scales are: L (Lie Scale), F (False Item Scale) and K (Defensiveness Scale). In contrast to the MMPI-2’s clinical scales, there is a relative lack of pain research investigating whether clinically meaningful validity scale code-types might emerge. To examine this question, the MMPI-2 validity scales L, F and K were obtained from 400 chronic headache and orofacial pain patients and a K-means cluster analysis was performed. Analysis revealed three “good” validity scale clusters and a fourth cluster that did not achieve significance: Cluster 1 Hyperdefensive Code-type, included 127 participants (31%) and final cluster centers showed elevated L (63.1) and K (61.4) scores and slightly low F (48.8) scores. Cluster 2, Normal Code-type, included 165 participants (40%) and final centers showed somewhat low L (46.8) scores, and normal F (49.4) and K (51.0) scores. Cluster 3, Distressed, included 92 participants (about 22% of the sample) and final cluster centers reflected normal L (50.2) scores, elevated F (69.50) scores and low K (44.6) scores. Cluster 4, Severe Distress or Malingering, included only 22 participants, 5% of the of the total sample. Final cluster centers showed somewhat low L (47.9) scores, very high F (100.0) scores and very low K (36.3) scores. These validity scale code-types point to distinct differences in how chronic pain patents may approach taking the MMPI-2, thus impacting test validity and accuracy of interpretation.

Improved Prototype Selection in Synergetic Pattern Recognition to Recognize Human Face Expressions

The prototype selection plays critical roles in synergetic pattern recognition (SPR). K-means clustering is widely adopted to determine appropriate prototypes in SPR. However, the selection of initial cluster centers significantly affects clustering results. We propose an improved k-means clustering to handle this challenge. According to inner-class distances among samples within the same cluster, we will dynamically adjust inter-class distances among clusters. Initial cluster centers will then be highly representative in that they are distributed among as many samples as possible. Consequently, local optima that are common in k-means clustering can be effectively reduced. After we obtain final cluster centers output from the improved k-means clustering, we then use these centers as the prototype vector to train a synergetic neural network (SNN), which will be utilized to recognize human face expressions. Experimental results demonstrate that our algorithm greatly improves the accuracy in recognizing face expressions and, in a more efficient manner.

Privacy Preserving Two-Party Hierarchical Clustering Over Vertically Partitioned Dataset

Data mining has been a popular research area for more than a decade. There are several problems associated with data mining. Among them clustering is one of the most interesting problems. However, this problem becomes more challenging when dataset is distributed between different parties and they do not want to share their data. So, in this paper we propose a privacy preserving two party hierarchical clustering algorithm vertically partitioned data set. Each site only learns the final cluster centers, but nothing about the individual’s data.

Efficient Evolutionary Data Mining Algorithms Applied to the Insurance Fraud Prediction

This study proposes two kinds of Evolutionary Data Mining (EvoDM) algorithms to the insurance fraud prediction. One is GA-Kmeans by combining K-means algorithm with genetic algorithm (GA). The other is MPSO-Kmeans by combining K-means algorithm with Momentum-type Particle Swarm Optimization (MPSO). The dataset used in this study is composed of 6 attributes with 5000 instances for car insurance claim. These 5000 instances are divided into 4000 training data and 1000 test data. Two different initial cluster centers for each attribute are set by means of (a) selecting the centers randomly from the training set and (b) averaging all data of training set, respectively. Thereafter, the proposed GA-Kmeans and MPSO-Kmeans are employed to determine the optimal weights and final cluster centers for attributes, and the accuracy of prediction for test set is computed based on the optimal weights and final cluster centers. Results show that the presented two EvoDM algorithms significantly enhance the accuracy of insurance fraud prediction when compared the results to that of pure K-means algorithm.

A Credit Rating Model for Enterprises Based on Projection Pursuit and K-Means Clustering Algorithm

This paper proposes a new credit rating model for enterprises based on Projection Pursuit and K-means clustering algorithm. Firstly, using Projection Pursuit, the comprehensive credit score of each sample is obtained, so as to reflect the structure or characteristics of original multi-dimensional data. Secondly, the distribution density of the comprehensive credit score series is estimated by the kernel density estimation method, and then the initial cluster centers in original high dimension space are determined according to the local maximum points of density function. Finally, starting from the initial cluster centers above, using K-means clustering algorithm, the final cluster centers are obtained, and then the credit grades are partitioned. Thus, the credit rating for enterprises is realized. Taking the high-tech listed companies in China as samples, it is proved that the model proposed by this paper is feasible and effective.

Data clustering using bacterial foraging optimization

Clustering divides data into meaningful or useful groups (clusters) without any prior knowledge. It is a key technique in data mining and has become an important issue in many fields. This article presents a new clustering algorithm based on the mechanism analysis of Bacterial Foraging (BF). It is an optimization methodology for clustering problem in which a group of bacteria forage to converge to certain positions as final cluster centers by minimizing the fitness function. The quality of this approach is evaluated on several well-known benchmark data sets. Compared with the popular clustering method named k-means algorithm, ACO-based algorithm and the PSO-based clustering technique, experimental results show that the proposed algorithm is an effective clustering technique and can be used to handle data sets with various cluster sizes, densities and multiple dimensions.

Communication-Efficient Privacy-Preserving Clustering

The ability to store vast quantities of data and the emergence of high speed networking have led to intense interest in distributed data mining. However, privacy concerns, as well as regulations, often prevent the sharing of data between multiple parties. Privacy-preserving distributed data mining allows the cooperative computation of data mining algorithms without requiring the participating organizations to reveal their individual data items to each other. This paper makes several contributions. First, we present a simple, deterministic, I/O-efficient kclustering algorithm that was designed with the goal of enabling an efficient privacy-preserving version of the algorithm. Our algorithm examines each item in the database only once and uses only sequential access to the data. Our experiments show that this algorithm produces cluster centers that are, on average, more accurate than the ones produced by the well known iterative k-means algorithm, and compares well against BIRCH. Second, we present a distributed privacy-preserving protocol for k-clustering based on our new clustering algorithm. The protocol applies to databases that are horizontally partitioned between two parties. The participants of the protocol learn only the final cluster centers on completion of the protocol. Unlike most of the earlier results in privacy-preserving clustering, our protocol does not reveal intermediate candidate cluster centers. The protocol is also efficient in terms of communication and does not depend on the size of the database. Although there have been other clustering algorithms that improve on the k-means algorithm, ours is the first for which a communication efficient cryptographic privacy-preserving protocol has been demonstrated.

Fast fuzzy clustering

This paper presents a multistage random sampling fuzzy c-means-based clustering algorithm, which significantly reduces the computation time required to partition a data set into c classes. A series of subsets of the full data set are used to create initial cluster centers in order to provide an approximation to the final cluster centers. The quality of the final partitions is equivalent to those created by fuzzy c-means. The speed-up is normally a factor of 2–3 times, which is especially significant for high-dimensional spaces and large data sets. Examples of the improved speed of the algorithm in two multi-spectral domains, magnetic resonance image segmentation and satellite image segmentation, are given. The results are compared with fuzzy c-means in terms of both the time required and the final resulting partition. Significant speedup is shown in each example presented in the paper. Further, the convergence properties of fuzzy c-means are preserved.