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.

Oil Family Typing Using a Hybrid Model of Self-Organizing Map and Artificial Neural Network

Identifying the number of oil families in petroleum basins provides practical and valuable information in petroleum geochemistry studies from exploration to development. Oil family grouping helps us track migration pathways, identify the number of active source rock(s), and examine the reservoir continuity. To date, almost in all oil family typing studies, common statistical methods such as principal component analysis (PCA) and hierarchical clustering analysis (HCA) have been used. However, there is no publication regarding using artificial neural networks (ANNs) for examining the oil families in petroleum basins. Hence, oil family typing requires novel, not overused and common techniques. This paper is the first report of oil family typing using ANNs as robust computational methods. To this end, a self-organization map (SOM) neural network associated with three clustering validity indices were employed on oil samples belonging to the Iranian part of the Persian Gulf’ oilfields. For the SOM network, at first, ten default clusters were selected. Afterwards, three effective clustering validity coefficients, namely Calinski-Harabasz (CH), Silhouette indexes (SI) and Davies-Bouldin (DB), were operated to find the optimum number of clusters. Accordingly, among ten default clusters, the maximum CH (62) and SI (0.58) were acquired for four clusters. Likewise, the lowest DB (0.8) was obtained for four clusters. Thus, all three validation coefficients introduced four clusters as the optimum number of clusters or oil families. The number of oil families identified in the present report is consistent with those previously reported by other researchers in the same study area. However, the techniques used in the present paper, which have not been implemented so far, can be introduced as more straightforward for clustering purposes in the oil family typing than those of common and overused methods of PCA and HCA.

Oil Family Typing Using a Hybrid Model of Self-Organizing Map and Artificial Neural Network

Identifying the number of oil families in petroleum basins provides practical and valuable information in petroleum geochemistry studies from exploration to development. Oil family grouping helps us track migration pathways, identify the number of active source rock(s), and examine the reservoir continuity. To date, almost in all oil family typing studies, common statistical methods such as principal component analysis (PCA) and hierarchical clustering analysis (HCA) have been used. However, there is no publication regarding using artificial neural networks (ANNs) for examining the oil families in petroleum basins. Hence, oil family typing requires novel, not overused and common techniques. This paper is the first report of oil family typing using ANNs as robust computational methods. To this end, a self-organization map (SOM) neural network associated with three clustering validity indices were employed on oil samples belonging to the Iranian part of the Persian Gulf’ oilfields. For the SOM network, at first, ten default clusters were selected. Afterwards, three effective clustering validity coefficients, namely Calinski-Harabasz (CH), Silhouette indexes (SI) and Davies-Bouldin (DB), were operated to find the optimum number of clusters. Accordingly, among ten default clusters, the maximum CH (62) and SI (0.58) were acquired for four clusters. Likewise, the lowest DB (0.8) was obtained for four clusters. Thus, all three validation coefficients introduced four clusters as the optimum number of clusters or oil families. The number of oil families identified in the present report is consistent with those previously reported by other researchers in the same study area. However, the techniques used in the present paper, which have not been implemented so far, can be introduced as more straightforward for clustering purposes in the oil family typing than those of common and overused methods of PCA and HCA.

Investigating cluster validation metrics for optimal number of clusters determination

In short, clustering is the process of partitioning a given set of objects into groups containing highly related instances. This relation is determined by a specific distance metric with which the intra-cluster similarity is estimated. Finding an optimal number of such partitions is usually the key step in the entire process, yet a rather difficult one. Selecting an unsuitable number of clusters might lead to incorrect conclusions and, consequently, to wrong decisions: the term “optimal” is quite ambiguous. Furthermore, various inherent characteristics of the datasets, such as clusters that overlap or clusters containing subclusters, will most often increase the level of difficulty of the task. Thus, the methods used to detect similarities and the parameter selection of the partition algorithm have a major impact on the quality of the groups and the identification of their optimal number. Given that each dataset constitutes a rather distinct case, validity indices are indicators introduced to address the problem of selecting such an optimal number of clusters. In this work, an extensive set of well-known validity indices, based on the approach of the so-called relative criteria, are examined comparatively. A total of 26 cluster validation measures were investigated in two distinct case studies: one in real-world and one in artificially generated data. To ensure a certain degree of difficulty, both real-world and generated data were selected to exhibit variations and inhomogeneity. Each of the indices is being deployed under the schemes of 9 different clustering methods, which incorporate 5 different distance metrics. All results are presented in various explanatory forms.

Psychometric Properties of SCARED-C Scale in a Romanian Community Sample and its Future Utility for Dental Practice

SCARED-C instrument (the child version, 41 items) is used for screening anxiety in children between 8 to 18 years old and has been first introduced by Birmaher & collab. in 1995, with good psychometric data - internal consistency from α =.74 to .93 - and good discriminative validity indices in the original versions (1997, 1999). Since then, many countries have adopted the scale, for its utility in identifying five subsets of anxiety disorders (subscales): somatic/panic disorder, generalized anxiety, separation anxiety, social phobia, and school avoidance. The present study contains the first Romanian translated and adapted version of the SCARED-C instrument on a community sample of 477 children (8-18 years old) from Mureș county schools. The instrument showed moderate to good internal consistency (α Cronbach from to .63 to .91 for the total scale) and good test-retest reliability (.70) on a subset of 85 children sample. A confirmatory factorial analysis (CFA) was conducted to test the factor structure of the Romanian version of SCARED-C; results showed that SCARED-C has good psychometric properties to be used for screening anxiety in Romanian children and adolescents. The implications for using SCARED-C in dental practice are discussed. Future studies need to be conducted for exploring convergent and discriminative validity of the instrument and the sensitivity to current DSM-V criteria. Application on a dental pediatric sample is also required.

Evaluation of Validity Indices of Spiritual Competence Questionnaire

Introduction: Spiritual competence in personal and occupational lives increases compatibility, adaptation, problem-solving skills, finding meaning in life events, inner and outer peace, dynamism, and vitality and can have positive effects on other aspects of life. The first step to improve the status of spiritual competence in society is its evaluation, with currently few tools in Persian for its measurement. Therefore, this study aimed to translate and validate the Spiritual Competence Questionnaire. Methods: After translation to Persian, the formal validity of this questionnaire was inference using the opinions of nine experts based on Waltz and Basel and Lawshe’s theory. Moreover, 179 individuals completed the study questionnaire to measure the reliability and perform exploratory factor analysis (EFA). Microsoft Excel (2016) and SPSS software (version 24) were used for statistical analysis. Results: The validity of this questionnaire in two dimensions was 90% (content validity index; simplicity of questions: 88%, relevance of questions: 96%, and clarity of questions: 87%) and 97% (content validity ratio: standard). The reliability of this tool, which was calculated based on Cronbach’s alpha, was 89%. The Kaiser-Meyer-Olkin index in EFA was 88%, and the mean variance for seven factors was 67%. Conclusions: In addition to good reliability and validity, this tool is quite simple and fluent. Furthermore, with the possibility to be completed in 15 - 20 min, the questionnaire has the necessary features to assess the spiritual worthiness of individuals in different age groups.

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.

Multi-Objective Artificial Bee Colony Algorithm for Parameter-Free Neighborhood-Based Clustering

Although various clustering algorithms have been proposed, most of them cannot handle arbitrarily shaped clusters with varying density and depend on the user-defined parameters which are hard to set. In this paper, to address these issues, the authors propose an automatic neighborhood-based clustering approach using an extended multi-objective artificial bee colony (NBC-MOABC) algorithm. In this approach, the ABC algorithm is used as a parameter tuning tool for the NBC algorithm. NBC-MOABC is parameter-free and uses a density-based solution encoding scheme. Furthermore, solution search equations of the standard ABC are modified in NBC-MOABC, and a mutation operator is used to better explore the search space. For evaluation, two objectives, based on density concepts, have been defined to replace the conventional validity indices, which may fail in the case of arbitrarily shaped clusters. Experimental results demonstrate the superiority of the proposed approach over seven clustering methods.