The Minimum Description Length Principle for Pattern Mining: A Survey

This is about the Minimum Description Length (MDL) principle applied to pattern mining. The length of this description is kept to the minimum. Mining patterns is a core task in data analysis and, beyond issues of efficient enumeration, the selection of patterns constitutes a major challenge. The MDL principle, a model selection method grounded in information theory, has been applied to pattern mining with the aim to obtain compact high-quality sets of patterns. After giving an outline of relevant concepts from information theory and coding, as well as of work on the theory behind the MDL and similar principles, we review MDL-based methods for mining various types of data and patterns. Finally, we open a discussion on some issues regarding these methods, and highlight currently active related data analysis problems

Learning ontology aware classifiers

Many applications of data-driven knowledge discovery processes call for the exploration of data from multiple points of view that reflect different ontological commitments on the part of the learner. Of particular interest in this context are algorithms for learning classifiers from ontologies and data. Against this background, my dissertation research is aimed at the design and analysis of algorithms for construction of robust, compact, accurate and ontology aware classifiers. We have precisely formulated the problem of learning pattern classifiers from attribute value taxonomies (AVT) and partially specified data. We have designed and implemented efficient and theoretically well-founded AVT-based classifier learners. Based on a general strategy of hypothesis refinement to search in a generalized hypothesis space, our AVT-guided learning algorithm adopts a general learning framework that takes into account the tradeoff between the complexity and the accuracy of the predictive models, which enables us to learn a classifier that is both compact and accurate. We have also extended our approach to learning compact and accurate classifier from semantically heterogeneous data sources. We presented a principled way to reduce the problem of learning from semantically heterogeneous data to the problem of learning from distributed partially specified data by reconciling semantic heterogeneity using AVT mappings, and we described a sufficient statistics based solution

Feature Selection Based on Sequential Orthogonal Search Strategy

This thesis introduces three new feature selection methods based on sequential orthogonal search strategy that addresses three different contexts of feature selection problem being considered. The first method is a supervised feature selection called the maximum relevance–minimum multicollinearity (MRmMC), which can overcome some shortcomings associated with existing methods that apply the same form of feature selection criterion, especially those that are based on mutual information. In the proposed method, relevant features are measured by correlation characteristics based on conditional variance while redundancy elimination is achieved according to multiple correlation assessment using an orthogonal projection scheme. The second method is an unsupervised feature selection based on Locality Preserving Projection (LPP), which is incorporated in a sequential orthogonal search (SOS) strategy. Locality preserving criterion has been proved a successful measure to evaluate feature importance in many feature selection methods but most of which ignore feature correlation and this means these methods ignore redundant features. This problem has motivated the introduction of the second method that evaluates feature importance jointly rather than individually. In the method, the first LPP component which contains the information of local largest structure (LLS) is utilized as a reference variable to guide the search for significant features. This method is referred to as sequential orthogonal search for local largest structure (SOS-LLS). The third method is also an unsupervised feature selection with essentially the same SOS strategy but it is specifically designed to be robust on noisy data. As limited work has been reported concerning feature selection in the presence of attribute noise, the third method is thus attempts to make an effort towards this scarcity by further exploring the second proposed method. The third method is designed to deal with attribute noise in the search for significant features, and kernel pre-images (KPI) based on kernel PCA are used in the third method to replace the role of the first LPP component as the reference variable used in the second method. This feature selection scheme is referred to as sequential orthogonal search for kernel pre-images (SOS-KPI) method. The performance of these three feature selection methods are demonstrated based on some comprehensive analysis on public real datasets of different characteristics and comparative studies with a number of state-of-the-art methods. Results show that each of the proposed methods has the capacity to select more efficient feature subsets than the other feature selection methods in the comparative studies

Design and analysis of scalable rule induction systems

Machine learning has been studied intensively during the past two decades. One motivation has been the desire to automate the process of knowledge acquisition during the construction of expert systems. The recent emergence of data mining as a major application for machine learning algorithms has led to the need for algorithms that can handle very large data sets. In real data mining applications, data sets with millions of training examples, thousands of attributes and hundreds of classes are common. Designing learning algorithms appropriate for such applications has thus become an important research problem. A great deal of research in machine learning has focused on classification learning. Among the various machine learning approaches developed for classification, rule induction is of particular interest for data mining because it generates models in the form of IF-THEN rules which are more expressive and easier for humans to comprehend. One weakness with rule induction algorithms is that they often scale relatively poorly with large data sets, especially on noisy data. The work reported in this thesis aims to design and develop scalable rule induction algorithms that can process large data sets efficiently while building from them the best possible models. There are two main approaches for rule induction, represented respectively by CN2 and the AQ family of algorithms. These approaches vary in the search strategy employed for examining the space of possible rules, each of which has its own advantages and disadvantages. The first part of this thesis introduces a new rule induction algorithm for learning classification rules, which broadly follows the approach of algorithms represented by CN2. The algorithm presents a new search method which employs several novel search-space pruning rules and rule-evaluation techniques. This results in a highly efficient algorithm with improved induction performance. Real-world data do not only contain nominal attributes but also continuous attributes. The ability to handle continuously valued data is thus crucial to the success of any general purpose learning algorithm. Most current discretisation approaches are developed as pre- processes for learning algorithms. The second part of this thesis proposes a new approach which discretises continuous-valued attributes during the learning process. Incorporating discretisation into the learning process has the advantage of taking into account the bias inherent in the learning system as well as the interactions between the different attributes. This in turn leads to improved performance. Overfitting the training data is a major problem in machine learning, particularly when noise is present. Overfitting increases learning time and reduces both the accuracy and the comprehensibility of the generated rules, making learning from large data sets more difficult. Pruning is a technique widely used for addressing such problems and consequently forms an essential component of practical learning algorithms. The third part of this thesis presents three new pruning techniques for rule induction based on the Minimum Description Length (MDL) principle. The result is an effective learning algorithm that not only produces an accurate and compact rule set, but also significantly accelerates the learning process. RULES-3 Plus is a simple rule induction algorithm developed at the author's laboratory which follows a similar approach to the AQ family of algorithms. Despite having been successfully applied to many learning problems, it has some drawbacks which adversely affect its performance. The fourth part of this thesis reports on an attempt to overcome these drawbacks by utilising the ideas presented in the first three parts of the thesis. A new version of RULES-3 Plus is reported that is a general and efficient algorithm with a wide range of potential applications