Algorithmic Issues.- Inferring a Boolean Function from Positive and Negative Examples.- A Revised Branch-and-Bound Approach for Inferring a Boolean Function from Examples.- Some Fast Heuristics for Inferring a Boolean Function from Examples.- An Approach to Guided Learning of Boolean Functions.- An Incremental Learning Algorithm for Inferring Boolean Functions.- A Duality Relationship Between Boolean Functions in CNF and DNF Derivable from the Same Training Examples.- The Rejectability Graph of Two Sets of Examples.- Application Issues.- The Reliability Issue in Data Mining: The Case of Computer-Aided Breast Cancer Diagnosis.- Data Mining and Knowledge Discovery by Means of Monotone Boolean Functions.- Some Application Issues of Monotone Boolean Functions.- Mining of Association Rules.- Data Mining of Text Documents.- First Case Study: Predicting Muscle Fatigue from EMG Signals.- Second Case Study: Inference of Diagnostic Rules for Breast Cancer.- A Fuzzy Logic Approach to Attribute Formalization: Analysis of Lobulation for Breast Cancer Diagnosis.- Conclusions.
The importance of having ef cient and effective methods for data mining and kn- ledge discovery (DM&KD), to which the present book is devoted, grows every day and numerous such methods have been developed in recent decades. There exists a great variety of different settings for the main problem studied by data mining and knowledge discovery, and it seems that a very popular one is formulated in terms of binary attributes. In this setting, states of nature of the application area under consideration are described by Boolean vectors de ned on some attributes. That is, by data points de ned in the Boolean space of the attributes. It is postulated that there exists a partition of this space into two classes, which should be inferred as patterns on the attributes when only several data points are known, the so-called positive and negative training examples. The main problem in DM&KD is de ned as nding rules for recognizing (cl- sifying) new data points of unknown class, i. e. , deciding which of them are positive and which are negative. In other words, to infer the binary value of one more attribute, called the goal or class attribute. To solve this problem, some methods have been suggested which construct a Boolean function separating the two given sets of positive and negative training data points.
Using a novel method, the monograph studies a series of interconnected key data mining and knowledge discovery problems
Provides a unique perspective into the essence of some fundamental Data Mining issues, many of which come from important real life applications
Applications and algorithms are accompanied by experimental results