Keystroke Dynamics or typing dynamics refers to the method of identifying or confirming the identity of a person based on the typing pattern by checking the various timing information obtained when a key is pressed and released. It has been hypothesized that a user's keystroke patterns change according to his/her emotions. However, there were only limited investigations about the phenomenon itself in previous studies. The work in this paper is based on the use of auditory stimuli to check the influence of keystroke patterns and its variations according to the emotions of an individual. The advantages of using this method are that the data collected through this approach is non-intrusive and easy to obtain. The proposed system is of a controlled experiment to collect keystroke data from multiple subjects in a variety of emotional states induced by International Affective Digitized Sounds (IADS) using an Android Application. To examine the data collected, Two-way Valence (3) x Arousal (3) ANOVAs is applied. The work in this paper aims to prove that keystroke duration and latency are influenced by valence and arousal.
Clustering is data mining technique of grouping objects or data into clusters in which objects within the cluster have high similarity, but are very dissimilar to objects in the other clusters. Similarities and dissimilarities are measured on the attribute values which describe the objects. Clustering methods are used to formulate and typecast the data, for data compression and model construction, for detection of outliers etc. Common approach of all clustering methods is to find clusters centre which represent each cluster. Data set can be numeric or categorical. Numeric data can be oppressed to naturally define distance function between data points, whereas categorical data can be borrowed from either quantitative or qualitative data where observations are directly observed from counts. The work done here revolves around the implementation of selected latest clustering algorithms, a study of various pros and cons of the same, and also a comparative analysis of these clustering techniques.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.