Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

Distributed Learning for Multiple Source Data

Distributed learning is the problem of inferring a function when data to be analyzed is distributed across a network of agents. Separate domains of application may largely impose different constraints on the solution, including low computational power at every location, limited underlying connectivity (e.g. no broadcasting capability) or transferability constraints related to the enormous bandwidth requirement. Thus, it is no longer possible to send data in a central node where traditionally learning algorithms are used, while new techniques able to model and exploit locally the information on big data are necessary. Motivated by these observations, this thesis proposes new techniques able to efficiently overcome a fully centralized implementation, without requiring the presence of a coordinating node, while using only in-network communication. The focus is given on both supervised and unsupervised distributed learning procedures that, so far, have been addressed only in very specific settings only. For instance, some of them are not actually distributed because they just split the calculation between different subsystems, others call for the presence of a fusion center collecting at each iteration data from all the agents; some others are implementable only on specific network topologies such as fully connected graphs. In the first part of this thesis, these limits have been overcome by using spectral clustering, ensemble clustering or density-based approaches for realizing a pure distributed architecture where there is no hierarchy and all agents are peer. Each agent learns only from its own dataset, while the information about the others is unknown and obtained in a decentralized way through a process of communication and collaboration among the agents. Experimental results, and theoretical properties of convergence, prove the effectiveness of these proposals. In the successive part of the thesis, the proposed contributions have been tested in several real-word distributed applications. Telemedicine and e-health applications are found to be one of the most prolific area to this end. Moreover, also the mapping of learning algorithms onto low-power hardware resources is found as an interesting area of applications in the distributed wireless networks context. Finally, a study on the generation and control of renewable energy sources is also analyzed. Overall, the algorithms presented throughout the thesis cover a wide range of possible practical applications, and trace the path to many future extensions, either as scientific research or technological transfer results

Faculty Publications and Creative Works 2004

Faculty Publications & Creative Works is an annual compendium of scholarly and creative activities of University of New Mexico faculty during the noted calendar year. Published by the Office of the Vice President for Research and Economic Development, it serves to illustrate the robust and active intellectual pursuits conducted by the faculty in support of teaching and research at UNM

Biological and biomimetic machine learning for automatic classification of human gait

Machine learning (ML) research has benefited from a deep understanding of biological mechanisms that have evolved to perform comparable tasks. Recent successes of ML models, superseding human performance in human perception based tasks has garnered interest in improving them further. However, the approach to improving ML models tends to be unstructured, particularly for the models that aim to mimic biology. This thesis proposes and applies a bidirectional learning paradigm to streamline the process of improving ML models’ performance in classification of a task, which humans are already adept at. The approach is validated taking human gait classification as the exemplar task. This paradigm possesses the additional benefit of investigating underlying mechanisms in human perception (HP) using the ML models. Assessment of several biomimetic (BM) and non-biomimetic (NBM) machine learning models on an intrinsic feature of gait, namely the gender of the walker, establishes a functional overlap in the perception of gait between HP and BM, selecting the Long-Short-Term-Memory (LSTM) architecture as the BM of choice for this study, when compared with other models such as support vector machines, decision trees and multi-layer perceptron models. Psychophysics and computational experiments are conducted to understand the overlap between human and machine models. The BM and HP derived from psychophysics experiments, share qualitatively similar profiles of gender classification accuracy across varying stimulus exposure durations. They also share the preference for motion-based cues over structural cues (BM=H>NBM). Further evaluation reveals a human-like expression of the inversion effect, a well-studied cognitive bias in HP that reduces the gender classification accuracy to 37% (p<0.05, chance at 50%) when exposed to inverted stimulus. Its expression in the BM supports the argument for learned rather than hard-wired mechanisms in HP. Particularly given the emergence of the effect in every BM, after training multiple randomly initialised BM models without prior anthropomorphic expectations of gait. The above aspects of HP, namely the preference for motion cues over structural cues and the lack of prior anthropomorphic expectations, were selected to improve BM performance. Representing gait explicitly as motion-based cues of a non-anthropomorphic, gender-neutral skeleton not only mitigates the inversion effect in BM, but also improves significantly the classification accuracy. In the case of gender classification of upright stimuli, mean accuracy improved by 6%, from 76% to 82% (F1,18 = 16, p<0.05). For inverted stimuli, mean accuracy improved by 45%, from 37% to 82% (F1,18 = 20, p<0.05). The model was further tested on a more challenging, extrinsic feature task; the classification of the emotional state of a walker. Emotions were visually induced in subjects through exposure to emotive or neutral images from the International Affective Picture System (IAPS) database. The classification accuracy of the BM was significantly above chance at 43% accuracy (p<0.05, chance at 33.3%). However, application of the proposed paradigm in further binary emotive state classification experiments, improved mean accuracy further by 23%, from 43% to 65% (F1,18 = 7.4, p<0.05) for the positive vs. neutral task. Results validate the proposed paradigm of concurrent bidirectional investigation of HP and BM for the classification of human gait, suggesting future applications for automating perceptual tasks for which the human brain and body has evolved

Computer Science & Technology Series : XVI Argentine Congress of Computer Science - Selected papers

CACIC’10 was the sixteenth Congress in the CACIC series. It was organized by the School of Computer Science of the University of Moron. The Congress included 10 Workshops with 104 accepted papers, 1 main Conference, 4 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 5 courses. (http://www.cacic2010.edu.ar/). CACIC 2010 was organized following the traditional Congress format, with 10 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of three chairs of different Universities. The call for papers attracted a total of 195 submissions. An average of 2.6 review reports were collected for each paper, for a grand total of 507 review reports that involved about 300 different reviewers. A total of 104 full papers were accepted and 20 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI