Informatica

In data mining research, outliers usually represent extreme values that deviate from other observations on data. The significant issue of existing outlier detection methods is that they only consider the object itself not taking its neighbouring objects into account to extract location features. In this paper, we propose an innovative approach to this issue. First, we propose the notions of centrality and centre-proximity for determining the degree of outlierness considering the distribution of all objects. We also propose a novel graph-based algorithm for outlier detection based on the notions. The algorithm solves the problems of existing methods, i.e. the problems of local density, micro-cluster, and fringe objects. We performed extensive experiments in order to confirm the effectiveness and efficiency of our proposed method. The obtained experimental results showed that the proposed method uncovers outliers successfully, and outperforms previous outlier detection methods.

This paper is concerned with design, implementation and verification of persistent purely functional data structures which are motivated by the representation of natural numbers using positional number systems. A new implementation of random-access list based on redundant segmented binary numbers is described. It uses 4 digits and an invariant which guarantees constant worst-case bounds for cons, head, and tail list operations as well as logarithmic time for lookup and update. The relationship of random-access list with positional number system is formalized and benefits of this analogy are demonstrated.

The paper presents a simple programming language and rewriting system called GENS. It is based on an extension of the λ-calculus called λE-calculus. GENS is a multiparadigm language: it has been used for definition of semantics and for implementation of functional, logical, procedural, and object-oriented languages. It also allows combining different programming paradigm styles in a single programming language.