Elena Ivannikova

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Ivannikova, Elena Semantic place prediction from the mobile phone data Jyväskylä: University of Jyväskylä, 2012, 66 p. Information Technology, Master’s Thesis Supervisor(s): Hämäläinen, Timo Extracting the meaning of the most significant places, which are frequently visited by a mobile user, is a relevant problem in mobile computing. Predicting semantic(More)
This paper presents an information-theoretic approach to variable selection for prediction of laboratory measurements of paper quality. Along with a well-known Principal Component Analysis we considered techniques for variable selection based on the classical Shannon Mutual Information and a novel Maximal Information Coefficient. A multilayer perceptron(More)
The problem of predicting next user location is one of the most interesting and important mobile data mining tasks. Potential applications of the ability to predict the user's moves range from improving the relevance of location-based recommendations and mobile advertising to network traffic planning and promotion of coordination for disaster relief. In(More)
Keystroke dynamics is one of the authentication mechanisms which uses natural typing pattern of a user for identification. In this work, we introduced Dependence Clustering based approach to user authentication using keystroke dynamics. In addition, we applied a k-NN-based approach that demonstrated strong results. Most of the existing approaches use only(More)
This article proposes a scalable version of the Dependence Clustering algorithm which belongs to the class of spectral clustering methods. The method is implemented in Apache Spark using GraphX API primitives. Moreover, a fast approximate diffusion procedure that enables algorithms of spectral clustering type in Spark environment is introduced. In addition,(More)
This article proposes Soft Dependence Clustering (SDC) algorithm which belongs to the class of spectral clustering methods. On each iteration, SDC performs a hierarchical clustering producing a binary split which greedily maximizes the group dependence score. One of the advantages of SDC is the fact that division of a group into two clusters is done based(More)
The study of the general structure of macroglobulins and 7S immunoglobulins of shark and hen by the polarisation fluorescence method shows that shark immunoglobulins have a compact general structure whereas 7S hen immunoglobulins have a fairly flexible general structure. It is seen from our evidence, as compared to previous data on other classes, that the(More)
This paper presents a methodology for selecting best groups of predictor variables based on regression trees. Test results of the developed methodology applied to industrial pilot paper machine data are presented. Specifically, the results list process variable groups, which are more valuable in predicting paper quality variables. The benefit of paper(More)
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