Proposal for a Pivot-Based Vehicle Trajectory Clustering Method

Given the large volume of georeferenced information generated and stored by many types of devices, the study and improvement of techniques capable of operating with these data is an area of great interest. The analysis of vehicular trajectories with the aim of forming clusters and identifying emerging patterns is very useful for characterizing and analyzing transportation flows in cities. This paper presents a new trajectory clustering method capable of identifying clusters of vehicular sub-trajectories in various sectors of a city. The proposed method is based on the use of an auxiliary structure to determine the correct location of the centroid of each group or set of sub-trajectories along the adaptive process. The proposed method was applied on three real databases, as well as being compared with other relevant methods, achieving satisfactory results and showing good cluster quality according to the Silhouette index.

Implementation of the Simple Additive Weighting Method in Determining Centroids in the Process of Clustering the Poor in Kakatpenjalin Village, Lamongan Regency

Clustering is an algorithm in a decision support system that functions to organize an object into groups of data. In the clustering process, of course, a cluster centre is needed by the desired data group. However, the clustering process has a problem. Related research states that the results of k-means clustering can influence the selection of cluster centre points. Random selection of cluster centre points can result in different clustering results in the same data group. Not only on k-means, but k-medoids also have the same problem. So that to produce a good cluster, you must start by choosing the right cluster centre. To solve this problem, the Simple Additive Weighting method is used to select the centre point of the cluster. Simple Additive Weighting selects the centre point of the cluster by adding and summarizing the dataset. The summation is done by giving weight to each criterion and each criterion has its alternative value. From this weighted addition, the final value will be obtained. From the sum of SAW, then one of the objects with the highest and lowest values ​​can be taken to serve as the centre of the cluster.

The Implementations of K-medoids Clustering for Higher Education Accreditation by Evaluation of Davies Bouldin Index Clustering

The need for data analysis in tertiary education every semester is needed, this is due to the increasingly large and uncontrolled data, on the other hand generally higher education does not yet have a data warehouse and big data analysis to maintain data quality at tertiary institutions is not easy, especially to estimate the results of university accreditation high, because the data continues to grow and is not controlled, the purpose of this study is to apply k-medoids clustering by applying the calculation of the weighting matrix of higher education accreditation with the data of the last 3 years namely length of study, average GPA, student and lecturer ratio and the number of lecturers according to the study program, so that it can predict accurate cluster results, the results of this study indicate that k-medoid clustering produces good cluster data results with an evaluation value of the Bouldin index davies cluster index of 0.407029478 and is said to be a good cluster result.

Segmentasi Buah Mangga Menggunakan MLE dan GMM Sebagai Klasterisasi Pixel

Segmentation divides an image into parts or segments that are simpler and more meaningful so they can be analyzed further. The solution that has been found is using the Maximum Likelihood Estimation (MLE) method and the Gausian Mixture Model. GMM is a clustering method. GMM is a function consisting of several Gaussian, each identified by k ∈ {1, ..., K}, where K is the number of clusters in our dataset. Maximum Likelihood estimation is a technique used to find a certain point to maximize a function, this technique is very widely used in estimating a data distribution parameter. Tests carried out using mango images with 10 different backgrounds. GMM will cluster the pixels of the mango image to produce averages and covariates. Then the average and covariance will be used by MLE to qualify each pixel of the mango image. In this study GMM and MLE tests were carried out to segment mangoes. Based on the results obtained, the GMM and MLE methods have an error rate of 13.07% for 3 clusters, 8.06% for 4 clusters, and 6.63% for 5 clusters and good cluster quality with silhouette coefficient values ​​of 0.37686 for 3 clusters, 0.29577 for 4 clusters, and 0.26162 for 5 clusters.

Mesh: A Supply Chain Solution with Locally Private Blockchain Transactions

A major line of research on blockchains is geared towards enhancing the privacy of transactions through anonymity using generic non-interactive proofs. However, there is a good cluster of application scenarios where complete anonymity is not desirable and accountability is in fact required. In this work, we utilize non-interactive proofs of knowledge of elliptic curve discrete logarithms to present membership and verifiable encryption proof, which offers plausible anonymity when combined with the regular signing process of the blockchain transactions. The proof system requires no trusted setup, both its communication and computation complexities are linear in the number of set members, and its security relies on the discrete logarithm assumption. As a use-case for this scenario, we present Mesh which is a blockchain-based framework for supply chain management using RFIDs. Finally, the confidentiality of the transacted information is realized using a lightweight key chaining mechanism implemented on RFIDs. We formally define and prove the main security features of the protocol, and report on experiments for evaluating the performance of the modified transactions for this system.

Analysis of Clustering for Grouping of Productive Industry by K-Medoid Method

With the number of existing data, would have difficulty in doing the classification and the classification of the existing data. To resolve the issue, one way to do clustering is with data mining using clustering technique. The purpose of this research is the importance of knowing the pattern of the production of an industry that can provide the decision and the construction of clustering patterns for development and industrial progress. The results of this research can provide recommendations to improve the development of industry, help the owners of industry to develop the industry to an increase in the number of production and product quality, improve the competitiveness of the owner of the industry in developing its products. In this research will use the K-Medoids algorithm for data grouping of the industry so that it will be found the information that can be used for the recommendations of the improvement of marketing. The results of clustering with the number of cluster 3 produces the first group contains 85 members, the second group contains 222 members and the third group numbered 3 members. The third group are classified as productive because it has a combination of the value of the production of the most high the results of clustering have the quality of purity worth 1 means good cluster quality.

A cluster-analytical approach toward real-world outcome in outpatients with stable schizophrenia

BackgroundThis study aims to empirically identify profiles of functioning, and the correlates of those profiles in a sample of patients with stable schizophrenia in a real-world setting. The second aim was to assess factors associated with best profile membership.MethodsThree hundred and twenty-three outpatients were enrolled in a cross-sectional study. A two-step cluster analysis was used to define groups of patients by using baseline values for the Heinrichs-Carpenter Quality of Life Scale (QLS) total score. Logistic regression was used to construct models of class membership.ResultsOur study identified three distinct clusters: 50.4% of patients were classified in the “moderate” cluster, 27.9% in the “poor” cluster, 21.7% in the “good” cluster. Membership in the “good” cluster versus the “poor” cluster was characterized by less severe negative (OR = .832) and depressive symptoms (OR = .848), being employed (OR = 2.414), having a long-term relationship (OR = .256), and treatment with second-generation antipsychotics (SGAs) (OR = 3.831). Nagelkerke R2 for this model was .777.ConclusionsUnderstanding which factors are associated with better outcomes may direct specific and additional therapeutic interventions, such as treatment with SGAs and supported employment, in order to enhance benefits for patients, as well as to improve the delivery of care in the community.

Clustering Based on NMTF Algorithm

NMTF(Normalizing Mapping Training Framework) operates by mapping initial cluster centers and then iteratively training points to clusters base on the proximate cluster center and updating cluster centers. we regard finding good cluster centers as a normalizing parameter estimation problem then constructing the parameters of other normalizing models yields a space of novel clustering methods. In this paper we propose the idea using abstract of a text to members of a data partition in place of estimation of cluster centers. The method can accurately reconstruct meaning representation group used to generate a given data set.

GAYA HIDUP PENGGUNA TELEPON SELULER BLACKBERRY

Fakultas Ekonomi dan Bisnis, Universitas Muhammadiyah MalangE-mail: [email protected] paper explains the lifestyle of the Blackberry mobile phone users. Analysis tool used ClusterAnalysis. Its using two assumptions as the benchmark of a good cluster that is to have an internalhomogeneity (within clusters); the similarities between member in one cluster and external heterogeneity(the between clusters), i.e. the difference between a single cluster with the other clusters. Analysislifestyle Blackberry cell phone users conducted on students at Muhammadiyah University ofMalang used questionnaires. The results shows three lifestyle groups, namely fulfilled, believer andmaker groups. Based on those results, it can be used as a guidance for performing the segmentationinto marketers, especially marketers of Blackberry mobile phones.Keywords: lifestyle, cluster analysis, fulfilled, believer, maker