Propositional Satisfiability Method in Rough Classification Modeling for Data Mining

The fundamental problem in data mining is whether the whole information available is always necessary to represent the information system (IS). The goal of data mining is to find rules that model the world sufficiently well. These rules consist of conditions over attributes value pairs called description and classification of decision attribute. However, the set of all decision rules generated from all conditional attributes can be too large and can contain many chaotic rules that are not appropriate for unseen object classification. Therefore the search for the best rules must be performed because it is not possible to determine the quality of all rules generated from the information systems. In rough set approach to data mining, the set of interesting rules are determined using a notion of reduct. Rules were generated from reducts through binding the condition attribute values of the object class from which the reduct is originated to the corresponding attribute. It is important for the reducts to be minimum in size. The minimal reducts will decrease the size of the conditional attributes used to generate rules. Smaller size of rules are expected to classify new cases more properly because of the larger support in data and in some sense the most stable and frequently appearing reducts gives the best decision rules. The main work of the thesis is the generation of classification model that contains smaller number of rules, shorter length and good accuracy. The propositional satisfiability method in rough classification model is proposed in this thesis. Two models, Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) to represent the minimal reduct computation problem were proposed. The models involved a theoretical formalism of the discemibility relation of a decision system (DS) into an Integer Programming (IP) model. The proposed models were embedded within the default rules generation framework and a new rough classification method was obtained. An improved branch and bound strategy is proposed to solve the SIP and DRIP models that pruned certain amount of search. The proposed strategy used the conflict analysis procedure to remove the unnecessary attribute assignments and determined the branch level for the search to backtrack in a nonchronological manner. Five data sets from VCI machine learning repositories and domain theories were experimented. Total number rules generated for the best classification model is recorded where the 30% of data were used for training and 70% were kept as test data. The classification accuracy, the number of rules and the maximum length of rules obtained from the SIPIDRIP method was compared with other rough set method such as Genetic Algorithm (GA), Johnson, Holte l R, Dynamic and Exhaustive method. Four of the datasets were then chosen for further experiment. The improved search strategy implemented the non-chronological backtracking search that potentially prunes the large portion of search space. The experimental results showed that the proposed SIPIDRIP method is a successful method in rough classification modeling. The outstanding feature of this method is the reduced number of rules in all classification models. SIPIDRIP generated shorter rules among other methods in most dataset. The proposed search strategy indicated that the best performance can be achieved at the lower level or shorter path of the tree search. SIPIDRIP method had also shown promising across other commonly used classifiers such as neural network and statistical method. This model is expected to be able to represent the knowledge of the system efficiently

Heterogeneous data source integration for smart grid ecosystems based on metadata mining

The arrival of new technologies related to smart grids and the resulting ecosystem of applications andmanagement systems pose many new problems. The databases of the traditional grid and the variousinitiatives related to new technologies have given rise to many different management systems with several formats and different architectures. A heterogeneous data source integration system is necessary toupdate these systems for the new smart grid reality. Additionally, it is necessary to take advantage of theinformation smart grids provide. In this paper, the authors propose a heterogeneous data source integration based on IEC standards and metadata mining. Additionally, an automatic data mining framework isapplied to model the integrated information.Ministerio de Economía y Competitividad TEC2013-40767-

Hybrid model using logit and nonparametric methods for predicting micro-entity failure

Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods (Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic variables complement financial ratios for bankruptcy prediction

Predicting Good Configurations for GitHub and Stack Overflow Topic Models

Software repositories contain large amounts of textual data, ranging from source code comments and issue descriptions to questions, answers, and comments on Stack Overflow. To make sense of this textual data, topic modelling is frequently used as a text-mining tool for the discovery of hidden semantic structures in text bodies. Latent Dirichlet allocation (LDA) is a commonly used topic model that aims to explain the structure of a corpus by grouping texts. LDA requires multiple parameters to work well, and there are only rough and sometimes conflicting guidelines available on how these parameters should be set. In this paper, we contribute (i) a broad study of parameters to arrive at good local optima for GitHub and Stack Overflow text corpora, (ii) an a-posteriori characterisation of text corpora related to eight programming languages, and (iii) an analysis of corpus feature importance via per-corpus LDA configuration. We find that (1) popular rules of thumb for topic modelling parameter configuration are not applicable to the corpora used in our experiments, (2) corpora sampled from GitHub and Stack Overflow have different characteristics and require different configurations to achieve good model fit, and (3) we can predict good configurations for unseen corpora reliably. These findings support researchers and practitioners in efficiently determining suitable configurations for topic modelling when analysing textual data contained in software repositories.Comment: to appear as full paper at MSR 2019, the 16th International Conference on Mining Software Repositorie

QCBA: Postoptimization of Quantitative Attributes in Classifiers based on Association Rules

The need to prediscretize numeric attributes before they can be used in association rule learning is a source of inefficiencies in the resulting classifier. This paper describes several new rule tuning steps aiming to recover information lost in the discretization of numeric (quantitative) attributes, and a new rule pruning strategy, which further reduces the size of the classification models. We demonstrate the effectiveness of the proposed methods on postoptimization of models generated by three state-of-the-art association rule classification algorithms: Classification based on Associations (Liu, 1998), Interpretable Decision Sets (Lakkaraju et al, 2016), and Scalable Bayesian Rule Lists (Yang, 2017). Benchmarks on 22 datasets from the UCI repository show that the postoptimized models are consistently smaller -- typically by about 50% -- and have better classification performance on most datasets

arules - A Computational Environment for Mining Association Rules and Frequent Item Sets

Mining frequent itemsets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases. The R package arules presented in this paper provides a basic infrastructure for creating and manipulating input data sets and for analyzing the resulting itemsets and rules. The package also includes interfaces to two fast mining algorithms, the popular C implementations of Apriori and Eclat by Christian Borgelt. These algorithms can be used to mine frequent itemsets, maximal frequent itemsets, closed frequent itemsets and association rules.