Evaluation Of Clustering Results Research Articles

Medoid Silhouette clustering with automatic cluster number selection

The evaluation of clustering results is difficult, highly dependent on the evaluated data set and the perspective of the beholder. There are many different clustering quality measures, which try to provide a general measure to validate clustering results. A very popular measure is the Silhouette. We discuss the efficient medoid-based variant of the Silhouette, perform a theoretical analysis of its properties, provide two fast versions for the direct optimization, and discuss the use to choose the optimal number of clusters. We combine ideas from the original Silhouette with the well-known PAM algorithm and its latest improvements FasterPAM. One of the versions guarantees equal results to the original variant and provides a run speedup of O(k2). In experiments on real data with 30000 samples and k= 100, we observed a 10464× speedup compared to the original PAMMEDSIL algorithm. Additionally, we provide a variant to choose the optimal number of clusters directly.

METHODICAL APPROACH TO CLUSTER CONFIGURATIONS OF PRODUCT VARIANTS OF COMPLEX PRODUCT PORTFOLIOS

AbstractCompanies are increasingly struggling to manage their complex product portfolios. Since they do not fully understand the complexity, intelligent solutions are required. Emerging technologies and tools offer new ways to deal with existing problems. With the help of clustering, similarities between product variants can be identified automatically, and complexity can be systematically reduced. This article aims to develop a methodological approach to identify correlations between product variants in complex product portfolios automatically by using clustering algorithms. The approach includes the systematic cleaning and transformation of product portfolio data. In addition, a guide for algorithm selection and evaluation of clustering results is presented. As the last step, the results are systematically analysed and visualised. To validate the methodical approach, it is applied to a real-world data set of a commercial vehicle manufacturer and the usefulness of the results is confirmed in an expert workshop.

RETRACTED ARTICLE: A study on e-commerce customer segmentation management based on improved K-means algorithm

With the continuous popularization of the network, the customer resources have to be valued if enterprises want to occupy a certain share in the field of e-commerce. However, the traditional clustering analysis method has obvious lag for the segmentation of e-commerce customers. Therefore, accurate and efficient customer segmentation management should be carried out for the large and complex data information of current e-commerce enterprises, so as to realize customer retention and potential customer mining and promote the efficient development of enterprises. On the basis of customer segmentation theory, for the shortcomings of traditional K-means algorithm, a new SAPK + K-means algorithm based on semi-supervised Affinity Propagation combined with classic K-means algorithm is proposed in combination with AP algorithm, which is applied to e-commerce customers for segmentation management. The results show that when the SAPK + K-means algorithm clusters the iris dataset and the ionosphere dataset, the clustering time is longer than the K-means algorithm and the AP algorithm, but the algorithm error rate in the standard data is significantly reduced and the correct number of clusters can be obtained. The main steps of SAPK + K-means algorithm applied to customer segmentation management including data acquisition, cluster analysis and analysis and evaluation of clustering results. The SAPK + K-means algorithm clusters the data information of an e-commerce customer to obtain four different customer types and proposes corresponding strategies for each type of customer. It is concluded that the SAPK + k-means algorithm can significantly improve the clustering quality of customer data information and improve the effectiveness of activities of e-commerce enterprises.

ClusterEnG: an interactive educational web resource for clustering and visualizing high-dimensional data.

BackgroundClustering is one of the most common techniques in data analysis and seeks to group together data points that are similar in some measure. Although there are many computer programs available for performing clustering, a single web resource that provides several state-of-the-art clustering methods, interactive visualizations and evaluation of clustering results is lacking.MethodsClusterEnG (acronym for Clustering Engine for Genomics) provides a web interface for clustering data and interactive visualizations including 3D views, data selection and zoom features. Eighteen clustering validation measures are also presented to aid the user in selecting a suitable algorithm for their dataset. ClusterEnG also aims at educating the user about the similarities and differences between various clustering algorithms and provides tutorials that demonstrate potential pitfalls of each algorithm.ConclusionsThe web resource will be particularly useful to scientists who are not conversant with computing but want to understand the structure of their data in an intuitive manner. The validation measures facilitate the process of choosing a suitable clustering algorithm among the available options. ClusterEnG is part of a bigger project called KnowEnG (Knowledge Engine for Genomics) and is available at http://education.knoweng.org/clustereng.

A Survey On Unsupervised Evaluation Criteria For Image Clustering Validation

The evaluation of clustering results is the most difficult and frustrating part of cluster analysis. The challenge is to validate the obtained results without any apriori information. Validity indexes are widely used approach for evaluation of clustering results. These approaches can use three criteria: i) external (also called supervised) criteria: this type is based on comparing the obtained results with a previously known result (frequently called ground truth) and compute the similarity, ii) internal criteria (also called unsupervised) criteria: estimate the quality of the result using internal information of the data alone, and iii) relative criteria: thismeans multiple usages of one of the two above types ofdifferent results and see which is better than the other. Therefore we can say: depending on the information available and the problem type, different types of indexes might be used for cluster validation. Sometimes due to the complexity of the datasets, one validity index is not sufficient to evaluate the quality of the obtained results, and then a combination of two or more index should be used. In this paper, a basic general review on evaluation criteria is first given and then the focus is spotted on unsupervised criteria as they are much more useful, thanks to their objective functionality.

A New Cluster Validity Index Using Novel Point-based Compactness Measure

Cluster validation is an important part of clustering process. This is one of the most widely studied problem and a number of methods and indices have been proposed from time to time. The evaluation of clustering results is very important for determining the optimal clustering solution for a given dataset. The most commonly used approaches for cluster validation are based on internal validity indices. In this paper, we propose a new cluster validity index (ARPoints index) for the purpose of cluster validation. The proposed index measures compactness of clusters by using a new ratio of actual and proportionate number of points present in a given space defined in this paper. We conduct a thorough comparison of these indices with the proposed index on a number of datasets which includes shaped and Gaussian-like datasets. Experimental results show that the proposed index performs better than the commonly known indices.

Quality prediction of multilingual news clustering: An experimental study

The evaluation of clustering results is one of the most important issues in cluster analysis, a core task for effective information access. There are two types of measures for evaluating the quality of clustering results: internal and external. External validity measures evaluate how well the clustering results match prior knowledge about the data, whereas internal measures do not need external information, dealing only with information within the data. In this regard, the main drawback of external evaluation measures is that they are not applicable in real-world situations. In this paper we present an experimental study to determine whether it is possible to predict the quality of multilingual news clustering results by means of an internal evaluation measure. We study whether the internal evaluation measure Expected Density correlates with the external measure F-measure, the most common way of evaluating clustering results. In the experiments, we use different data collections, clustering algorithms and similarity measures in order to determine their influence in the correlation between those measures. Regarding similarity measures, another important issue in clustering, we propose a new similarity measure to calculate how similar two news documents are. This measure is based on the Named Entities shared by both documents. The results show that correlation depends on several different factors, such as the type of collection, the granularity of the clusters, the type of algorithm and the similarity measure.

A Kind of Visual Speech Feature with the Geometric and Local Inner Texture Description

In this paper, we propose a type of joint feature with geometric parameters and color moments to represent the speaking-mouth frames for image-based visual speech synthesis systems. Based on FDP around the mouth area, the geometric feature is obtained by computing Euclidean distances to describe the width of the speaking mouth, the height of the outer and inner lips and the distances between them. The color moment component in the joint feature is obtained by calculating the texture between the upper and lower inner lips to describe the visibility state of the teeth. Through analyzing the accordance between the teeth visibility and the components of RGB and HSV color space based on the samples separately, we discovered that green and blue components are good at describing the change of teeth visibility. The experiments show that the proposed joint feature can effectively provide the basis for categorizing the different speaking states especially at the sense of lip shapes and tooth visibility. The evaluation of clustering results is done by analyzing the derived parameters of the silhouette function. The analyzing results prove that comparing with the geometric only and PCA, our proposed feature together with the shape and the local inner lip texture clues has better performance in improving the similarity between samples within the clusters. In the future, more expressive features with the shape and local texture information should be explored to increase the proportion of similar samples within the clusters to improve the descriptive ability of speaking mouths. DOI: http://dx.doi.org/10.11591/telkomnika.v11i2.2047

CVAP: Validation for Cluster Analyses

Evaluation of clustering results (or cluster validation) is an important and necessary step in cluster analysis, but it is often time-consuming and complicated work. We present a visual cluster validation tool, the Cluster Validity Analysis Platform (CVAP), to facilitate cluster validation. The CVAP provides necessary methods (e.g., many validity indices, several clustering algorithms and procedures) and an analysis environment for clustering, evaluation of clustering results, estimation of the number of clusters, and performance comparison among different clustering algorithms. It can help users accomplish their clustering tasks faster and easier and help achieve good clustering quality when there is little prior knowledge about the cluster structure of a data set.