A cluster analysis of basketball players for each of the five traditionally defined positions

Determining the players’ playing styles and bringing the right players together are very important for winning in basketball. This study aimed to group basketball players into similar clusters according to their playing styles for each of the traditionally defined five positions (point guard (PG), shooting guard (SG), small forward (SF), power forward (PF), and center (C)). This way, teams would be able to identify their type of players to help them determine what type of players they should recruit to build a better team. The 17 game-related statistics from 15 seasons of the National Basketball Association (NBA) were analyzed using a hierarchical clustering method. The cluster validity indices (CVIs) were used to determine the optimum number of groups. Based on this analysis, four clusters were identified for PG, SG, and SF positions, while five clusters for PF position and six clusters for C position were established. In addition to the definition of the created clusters, their individual achievements were examined based on three performance indicators: adjusted plus-minus (APM), average points differential, and the percentage of clusters on winning teams. This study contributes to the evaluation of team compatibility, which is a significant part of winning, as it allows one to determine the playing styles for each position, while examining the success of position pair combinations.

Using Fuzzy Clustering of User Perception to Determine the Number of Level-of-Service Categories for Bus Rapid Transit

The concept of level of service (LOS) is meant to reflect user perception of the quality of service provided by a transportation facility or service. Although the LOS of bus rapid transit (BRT) has received considerable attention, the number of levels of service of BRT that a user can perceive still remains unclear. Therefore, in this paper, we address this issue using fuzzy clustering of user perception. User perception is defined as a six-dimension vector of the perceived arrival time, perceived waiting time, bus speed perception, passenger load perception, perceived departure time, and overall perception. A smartphone-based transit travel survey system was developed, with which user perception surveys were conducted in three BRT systems in China. Fuzzy C-Means clustering, improved using a simulated annealing genetic algorithm, was adopted to partition user perception into two to ten clusters. Seven cluster validity indices were used to determine the appropriate number of LOS categories. Our results indicate that users can perceive two to four levels of service.

Clustering refinement

Advanced validation of cluster analysis is expected to increase confidence and allow reliable implementations. In this work, we describe and test CluReAL, an algorithm for refining clustering irrespective of the method used in the first place. Moreover, we present ideograms that enable summarizing and properly interpreting problem spaces that have been clustered. The presented techniques are built on absolute cluster validity indices. Experiments cover a wide variety of scenarios and six of the most popular clustering techniques. Results show the potential of CluReAL for enhancing clustering and the suitability of ideograms to understand the context of the data through the lens of the cluster analysis. Refinement and interpretability are both crucial to reduce failure and increase performance control and operational awareness in unsupervised analysis.

Comparison of Cluster Validity Index and Distance Measures Using Integrated Cluster Analysis and Structural Equation Modeling the Warp-PLS Approach

This study wants to compare the Integrated Cluster Analysis and SEM model of the Warp-PLS approach with various cluster validity indices and distance measures on Service Quality, Environment, Fashions, Willingness to Pay, and Compliant Paying Behavior of Bank X Customers. The data used in this study are primary. The variables used in this study are service quality, environment, fashion, willingness to pay, and compliance with paying behavior at Bank X. The data were obtained through a questionnaire with a Likert scale — measurement of variables in primary data using the average score of each item. The sampling technique used was purposive sampling. The object of observation is the customer as many as 100 respondents. Data analysis was carried out quantitatively, and a descriptive analysis was carried out first. An Integrated Cluster Analysis and SEM analysis of the Warp-PLS approach was carried out with the average linkage method on various cluster validity indices and three distance measures. The Warp-PLS approach's integrated cluster and SEM model with the Gap Index, Index C, Global Sillhouette, and Goodman-Kruskal with the Manhattan Distance are better than the Gap, Index C, Global Sillhouette, and Goodman-Kruskal with the Euclidean and Minkowski Distance. The novelty in this research is the application of Integrated Cluster Analysis and SEM of the Warp-PLS approach to compare 4 cluster validity indices, namely Gap Index, C Index, Global Sillhouette, and Goodman-Kruskal, and three distance measures, namely Euclidean, Manhattan, and Minkowski distances simultaneously.

Real-time classification of muscle signals from self-selected intentional movements

A novel method to automatically differentiate forearm movements has been proposed. The electromyography (EMG) signals were recorded from two muscle sites on the forearm in real-time. Two 2-dimensional feature spaces namely the natural logarithm of root-mean-square values (Log (RMS)), and the standard deviations of auto regressive model coefficients (Stdev (AR)) were created. The features were calculated within non-overlapping 0.2 second windows in real-time. The feature spaces were clustered using the fuzzy c-means algorithm [1]. The cluster multiplicities were investigated by five different cluster validity indices. Real-time EMG signal classification was achieved by calculating membership values. Log (RMS) performed superior to the Stdev (AR) feature space. The silhouette validity index provided the best cluster validity index in this study. On average, the proposed algorithm classified 4 movements with 92.7± 3.2% and 5 movements with 79.90%±16.8% accuracy. The algorithm also revealed the number of repeatable movements. It can also be adapted to daily variations in individual EMG signals.

Real-time classification of muscle signals from self-selected intentional movements

A novel method to automatically differentiate forearm movements has been proposed. The electromyography (EMG) signals were recorded from two muscle sites on the forearm in real-time. Two 2-dimensional feature spaces namely the natural logarithm of root-mean-square values (Log (RMS)), and the standard deviations of auto regressive model coefficients (Stdev (AR)) were created. The features were calculated within non-overlapping 0.2 second windows in real-time. The feature spaces were clustered using the fuzzy c-means algorithm [1]. The cluster multiplicities were investigated by five different cluster validity indices. Real-time EMG signal classification was achieved by calculating membership values. Log (RMS) performed superior to the Stdev (AR) feature space. The silhouette validity index provided the best cluster validity index in this study. On average, the proposed algorithm classified 4 movements with 92.7± 3.2% and 5 movements with 79.90%±16.8% accuracy. The algorithm also revealed the number of repeatable movements. It can also be adapted to daily variations in individual EMG signals.

Visualization and performance measure to determine number of topics in twitter data clustering using hybrid topic modeling

Topic models are widely used in building clusters of documents for more than a decade, yet problems occurring in choosing the optimal number of topics. The main problem is the lack of a stable metric of the quality of topics obtained during the construction of topic models. The authors analyzed from previous works, most of the models used in determining the number of topics are non-parametric and the quality of topics determined by using perplexity and coherence measures and concluded that they are not applicable in solving this problem. In this paper, we used the parametric method, which is an extension of the traditional topic model with visual access tendency for visualization of the number of topics (clusters) to complement clustering and to choose the optimal number of topics based on results of cluster validity indices. Developed hybrid topic models are demonstrated with different Twitter datasets on various topics in obtaining the optimal number of topics and in measuring the quality of clusters. The experimental results showed that the Visual Non-negative Matrix Factorization (VNMF) topic model performs well in determining the optimal number of topics with interactive visualization and in performance measure of the quality of clusters with validity indices.