Hércules Antonio do Prado

Emerging Technologies of Text Mining: Techniques and Applications

Massive amounts of textual data make up most organizations stored information. Therefore, there is increasingly high demand for a comprehensive resource providing practical hands-on knowledge for real-world applications. Emerging Technologies of Text Mining: Techniques and Applications provides the most recent technical information related to the computational models of the text mining process, discussing techniques within the realms of classification, association analysis, information extraction, and clustering. Offering an innovative approach to the utilization of textual information mining to maximize competitive advantage, Emerging Technologies of Text Mining: Techniques and Applications will provide libraries with the defining reference on this topic.

Rough Clustering: An Alternative to Find Meaningful Clusters by Using the Reducts from a Dataset

Rough Sets Theory has been applied to build classifiers by exploring symbolic relations in data. Indiscernibility relations combined with the concept notion, and the application of set operations, lead to knowledge discovery in an elegant and intuitive way. In this paper we argue that the indiscernibility relation has a strong appeal to be applied in clustering since itself is a sort of natural clustering in the n-dimensional space of attributes. We explore this fact to build a clustering scheme that discovers straight structures for clusters in the sub-dimensional space of the attributes. As the usual clustering process is a kind of search for concepts, the scheme here proposed provides a better description of such clusters allowing the analyst to figure out what cluster has meaning to be considered as a concept. The basic idea is to find reducts in a set of objects and apply them to any clustering procedure able to cope with discrete data. We apply the approach to a toy example of animal taxonomy in order to show its functionality.

On the Effectiveness of Candlestick Chart Analysis for the Brazilian Stock Market

Abstract Several techniques have been developed in pursuit of understanding the behavior of the financial market, in an attempt to predict the asset pricing behavior. The candlestick chart created in the 18th century is one of these techniques. In 2006, Greg Morris conducted a study on the effectiveness of this technique for the U.S. capital market. However, no similar work was done for the Brazilian market. In this paper, the behavior of part of the Brazilian capital market was studied using sixteen candlestick patterns. We considered the data series of ten stocks between 2005 and 2009, totaling approximately 40% of Ibovespa (Sao Paulo Stock Exchange Index) turnover. The frequency of confirmation of each pattern was measured along seven exchange sessions after occurrence of such pattern, and results were compared to those presented by Morris. Additionally, adjustments of the observed proportions of hits were tested for their statistical significance. Results found in the frequential analysis showed a discrepancy in relation to Morriss study. Likewise, in statistical analysis few patterns have confirmed the behavior expected of them. In at least one case the trend expressed by data, although significant, was contrary to the original interpretation of the pattern. Therefore, direct application of patterns developed for other markets, times or actions is not recommended. Such results do not allow for an affirmation that candlestick patterns have the power to predict future behavior of stocks traded in the Ibovespa stock market. However, we found statistically significant evidence of the predictive ability of some patterns, which may indicate that the technique must be adapted to the market where it is intended to be used. The main contributions of this paper are a partial replication of Morris’ study for a set of stocks traded in the Brazilian market, and a statistical analysis of the effectiveness of candlestick patterns as predictors of the behavior of those stocks.Click here and insert your abstract text.

Computational methods for agricultural research : advances and applications

This book brings computing solutions to ancient practices and modern concerns, sowing the seeds for a sustainable, constant food supply, utilizing cutting-edge computational techniques--Provided by publisher.

Improving image mining through Geoprocessing

Geoprocessing Information Systems (GIS) deal with structured information concerned some geographical localization. So one uses three-dimensional image representation systems in a huge database, where it is possible to insert many data about some interest domain, say, agriculture, economics, industry, demographics and so on. Images are powerful information sources that can soundly support decision making processes. An image can be seen as a set of elements with spatial localization and color. To interpret an image includes deriving clusters and relations between such elements. This article proposes an integration of Geoprocessing and Image Mining to support image based decisions in several domains such as healthcare.

Evolving a legacy data warehouse system to an object-oriented architecture

We introduce an object-oriented approach to transform a star-schema of a legacy data warehouse system into a Dimensional Object Model (DOM) (Firestone, 1998), in order to take advantage of the flexibility of the object paradigm. We have applied the Object-Oriented Software Engineering (OOSE) process, proposed by (Jacobson et al., 1992), to describe the life cycle of a data warehouse and to clarify the context of our intervention. By means of OOSE we also define the target architecture and the components that will ensure the development of reuse-supporting data warehouse systems. In a sense, a change tolerant architecture is proposed, as referred to by (Jacobson, 1998). The solution proposed applies the object paradigm for the star-schema decomposition in a three-layer architecture, separating the components in logical, interface and data management layers. This decomposition minimizes the dependence among the components and increases their reusability. The main contribution of this work is that, by transforming the star-schema into a DOM, we are preparing the system to evolve through any object-oriented data warehouse development methodology. In a context of many already existing data warehouse systems based on relational models, this transformation sounds especially interesting.

A WSA-based architecture for building multiagent systems

This paper discusses some advantages of applying the SOA paradigm for the MAS development, showing a framework whose architecture follows the WSA reference model. We believe that service-oriented paradigm can simplify the MAS development because it demands a much simpler coordination level than the traditional approaches focused on the message-oriented model.

Mining high performance managers based on the results of psychological tests

Selecting high performance managers represents a risky task mainly due to the costs involved in a wrong choice. This fact led to the development of many approaches to select the candidates that best fit into the requirements of a certain position. However, defining what are the most important features that condition a good personnel performance is still a problem. In this paper, we discuss an approach, based on data mining techniques, to help managers in this process. We built a classifier, based in the Combinatorial Neural Model (CNM), taking as dependent variable the performance of managers as observed along their careers. As independent variables, we considered the results of wellknown psychological tests (MBTI and DISC). The rules generated by CNM enabled the arising of interesting relations between the psychological profile of managers in their start point in the company and the quality of their work after some years in action. These rules are expected to support the improvement of the selection process by driving the choice of candidates to those with a best prospective. Also, the adequate allocation of people - the right professional in the right place - shall be improved.

Extracting definitions from brazilian legal texts

In order to avoid ambiguity and to ensure, as far as possible, a strict interpretation of law, legal texts usually define the specific lexical terms used within their discourse by means of normative rules. With an often large amount of rules in effect in a given domain, extracting these definitions manually would be a costly undertaking. This paper presents an approach to cope with this problem based in a variation of an automated technique of natural language processing of Brazilian Portuguese texts. For the sake of generality, the proposed solution was developed to address the more general problem of building a glossary from domain specific texts that contain definitions amongst their content. This solution was applied to a corpus of texts on the telecommunications regulations domain and the results are reported. The usual pipeline of natural language processing has been followed: preprocessing, segmentation, and part-of-speech tagging. A set of feature extraction functions is specified and used along with reference glossary information on whether or not a text fragment is a definition, to train a SVM classifier. At last, the definitions are extracted from the texts and evaluated upon a testing corpus, which also contains the reference glossary annotations on definitions. The results are then discussed in light of other definition extraction techniques.

Using data mining techniques for time estimation in software maintenance

Measuring and estimating are fundamental activities for the success of any project. In the software maintenance realm the lack of maturity, or even a low level of interest in adopting effective maintenance techniques and related metrics, has been pointed out as an important cause for the high costs involved. In this paper, data mining techniques are applied to provide a sound estimation for the time required to accomplish a maintenance task. Based on real-world data regarding maintenance requests, some regression models are built to predict the time required for each maintenance. Data on the team skill and the maintenance characteristics are mapped into values that predict better time estimations in comparison to the one predicted by the human expert. A particular finding from this research is that the time prediction provided by a human expert works as an inductive bias that improves the overall prediction accuracy of the models.