Academic literature on the topic 'Silhouette score value'

Data-driven problem-solving requires the capacity to use cutting-edge computational methods to explain fundamental phenomena to a large audience. These facilities are needed for political and social studies. Quantitative methods often involve knowledge of concepts, trends, and facts that affect the study programme. Researchers often don't know the data's structure or assumptions when analysing it. Data exploration may also obscure social science research methodology instruction. It was essential applied research before predictive modelling and hypothesis testing. Clustering is part of data mining and picking the right cluster count is key to improving predictive model accuracy for large datasets. Unsupervised machine learning (ML) algorithm K-means is popular. The method usually finds discrete, non-overlapping clusters with groups for each location. It can be difficult to choose the best k-means approach. In the human freedom index (HFI) dataset, the mini batch k-mean (MBK-mean) using the Hamely method reduces iteration and increases cluster efficiency. The silhouette score algorithm from Scikit-learn was used to obtain the average silhouette co-efficient of all samples for various cluster counts. A cluster with fewer negative values is considered best. Additionally, the silhouette with the greatest score has the optimum clusters.

In this paper, we scrutinize the effectiveness of various clustering techniques, investigating their applicability in Cultural Heritage monitoring applications. In the context of this paper, we detect the level of decomposition and corrosion on the walls of Saint Nicholas fort in Rhodes utilizing hyperspectral images. A total of 6 different clustering approaches have been evaluated over a set of 14 different orthorectified hyperspectral images. Experimental setup in this study involves K-means, Spectral, Meanshift, DBSCAN, Birch and Optics algorithms. For each of these techniques we evaluate its performance by the use of performance metrics such as Calinski-Harabasz, Davies-Bouldin indexes and Silhouette value. In this approach, we evaluate the outcomes of the clustering methods by comparing them with a set of annotated images which denotes the ground truth regarding the decomposition and/or corrosion area of the original images. The results depict that a few clustering techniques applied on the given dataset succeeded decent accuracy, precision, recall and f1 scores. Eventually, it was observed that the deterioration was detected quite accurately.

In this work, we introduce a new methodology to discover logistic regions for pricing. We use value-based characteristics from different sources, such as demographic, socioeconomic, risk, transportation, among others, to find homogeneous and valuable pricing regions. The problem was formulated as a traditional cluster solution, where well-know metrics, such as BIC and silhouette score, were used for technical validation, and business premises and constraints, operational and sales, where used to enrich feature engineering and refine cluster formation. The results presented here are from a preliminary work that was validated through several sessions with stakeholders of interest, but it is still missing the market validation. Indeed, this work will be deployed soon and a more detailed validation process, including client adherence, will be performed and monitored until the end of this year.

Human interactions with the trainer during physical training can be highly engaging and motivating [1] and are based on rapport as a dynamic structure of mutual attentiveness and coordination [2]. Human-Agent interaction in virtual reality (VR) aims to establish interaction patterns and rapport with virtual agents similar to real life. Research shows that users react towards virtual agents similar to real people [3] and that rapport is established similar to human rapport [4]. Therefore, rapport with virtual trainers in exergames is used to enhance an engaging and motivating user experience.In this paper we report on the results from an evaluation study on perceptions and interactions with the virtual trainer “Anna” after a 6-week exergame training for senior patients with hypertension. The human-like “Anna” is the key element of interaction design in a gamified series of exergames developed in the bewARe project. Anna was developed as a realistic, full body, female figure (silhouette) to motivate participation in the VR training. The primary goal of our research was to evaluate to what extent senior users can establish rapport with the virtual trainer as a factor contributing to positive user experience and training outcomes. The evaluation was conducted with 23 participants aged 65 and older with diagnosed hypertension. The virtual trainer Anna facilitated user participation in both exergames by giving instructions, modeling movements and providing feedback during the exergames in the HTC Vive Pro Headset. We used the 15-item rapport scale by [5] to measure rapport. The study also applied further research instruments to explore perceptions of the virtual trainer such as the trait list with 9 items describing selected features of the virtual trainer, and the bipolar uncanniness questionnaire with 40 adjectives used to assess possible Uncanny Valley effects described by [6]. The results of the rapport scale indicate that the design of the virtual trainer was effective for establishing rapport especially in terms of building a relationship with the virtual trainer and enhancing the engagement of senior users to participate in the VR training. However, the design was less effective in creating a positive perception of the trainer as a warm, caring and respectful agent. The overall median of the rapport scale was 6 (Min:1,Max:8). The results of the evaluation of the trait list revealed that voice quality, speech pauses and bodily movements were rated highest, followed by head and hand movements. The lowest values were researched for face expression. In the Uncanny Valley questionnaire, the median value for the humannes scale was 1 (Min:-3,Max:3), for the attractiveness 1 (Min:0,Max:3) and for the eeriness 0 (Min:-1,Max:0). Furthermore, the paper explores the relationships between the rapport scores and the perception of senior trainees of selected characteristics of the virtual agent and the uncanniness scale. Finally, given the diverse results from the study, the paper discusses possible design options for enhanced rapport and motivational effects of a virtual trainer based on the analysis of literature in related areas.References1. Ghosh, P., Satyawadi, R., Prasad Joshi, J., Ranjan, R. & Singh, P.: Towards more effective training programmes: a study of trainer attributes, In: Industrial and Commercial Training, vol. 44, pp. 194-202 (2012).2. Tickle-Degnen, L. & Rosenthal, R.: The Nature of Rapport and Its Nonverbal Correlates. In: Psychological Inquiry, vol. 1, pp. 285-293 (1990).3. Garau, M., Slater, M., Pertaub, D. P. & Razzaque, S. The responses of people to virtual humans in an immersive virtual environment. Presence. 14, pp. 104–116 (2005).4. Huang, L., Morency, L., & Gratch, J.: Virtual Rapport 2.0. In: Vilhjálmsson H. H., Kopp S., Marsella S., Thórisson K.R. (eds.) Intelligent Virtual Agents. IVA 2011. Lecture Notes in Computer Science, vol. 6895, pp. 68--79. Springer, Berlin, Heidelberg (2011).5. Gratch J., Wang N., Gerten J., Fast E. & Duffy R.: Creating Rapport with Virtual Agents. In: Pelachaud C., Martin JC., André E., Chollet G., Karpouzis K., Pelé D. (eds) Intelligent Virtual Agents. IVA 2007. Lecture Notes in Computer Science, vol 4722. Springer, Berlin, Heidelberg, (2007).6. Ho, C., & MacDorman, K.F.: Revisiting the uncanny valley theory: Developing and validating an alternative to the Godspeed indices. Comput. Hum. Behav., 26, pp. 1508-1518 (2010).