Clustering and selecting categorical features

In data clustering, the problem of selecting the subset of most relevant features from the data has been an active research topic. Feature selection for clustering is a challenging task due to the absence of class labels for guiding the search for relevant features. Most methods proposed for this goal are focused on numerical data. In this work, we propose an approach for clustering and selecting categorical features simultaneously. We assume that the data originate from a finite mixture of multinomial distributions and implement an integrated expectation-maximization (EM) algorithm that estimates all the parameters of the model and selects the subset of relevant features simultaneously. The results obtained on synthetic data illustrate the performance of the proposed approach. An application to real data, referred to official statistics, shows its usefulness

Gender Classification in Human Gait Using Support Vector Machine

Abstract. We describe an automated system that classifies gender by utilising a set of human gait data. The gender classification system consists of three stages: i) detection and extraction of the moving human body and its contour from im-age sequences; ii) extraction of human gait signature by the joint angles and body points; and iii) motion analysis and feature extraction for classifying gen-der in the gait patterns. A sequential set of 2D stick figures is used to represent the gait signature that is primitive data for the feature generation based on mo-tion parameters. Then, an SVM classifier is used to classify gender in the gait patterns. In experiments, higher gender classification performances, which are 96 % for 100 subjects, have been achieved on a considerably larger database.

Investigating the self-reported professional development activities of school leaders in Ghanaian rural basic schools

In Ghana, research has shown that educational reforms over the years have ignored the importance of school leadership development, and there are currently very few reform initiatives that address the need to develop the leadership proficiencies and skills of school leaders. This study sought to investigate how school leaders, who include headteachers, assistant headteachers, and form masters, in such contexts develop their leadership skills following their appointment into their new roles. A mixed-methods approach was employed in gathering both quantitative and qualitative data concurrently from three groups of leaders in the basic schools of one rural educational district in Ghana. Results from the study showed that the professional development activities (PDAs) that the leaders employed for their development were mostly informal and self-directed learning methods. There were only limited formal leadership development programmes for the leaders working within any of the rural district basic schools represented in the study. The study concludes that to strengthen school leadership in basic schools in the selected district, educational authorities would need to learn from international best practice to initiate sustainable PDAs to strengthen the leadership capacities of leaders to promote effective teaching and learning in schools.</p