Symbolic modeling of data with decision trees or decision rules has a certain appeal to data mining application developers. The computationally e cient nature of the modeling methodology, and the inbuilt explanatory nature of the models that are generated, are two often cited reasons for the preferred use of these methods. Traditionally, the applications of these methods had been restricted to ...

This paper addresses the important issue of the tradeoff between accuracy and comprehensibility in data mining. The paper presents results which show that it is, to some extent, possible to bridge this gap. A method for rule extraction from opaque models (Genetic Rule EXtraction – G-REX) is used to show the effects on accuracy when forcing the creation of comprehensible representations. In addi...

Come with us to read a new book that is coming recently. Yeah, this is a new coming book that many people really want to read will you be one of them? Of course, you should be. It will not make you feel so hard to enjoy your life. Even some people think that reading is a hard to do, you must be sure that you can do it. Hard will be felt when you have no ideas about what kind of book to read. Or...

In this paper, we present an adaptive modelling technique for studying past behaviors of objects and predicting the near fUture events. Our approach is to define a sliding window (of different window sizes) over a time sequence and build autoregression models from subsequences in different windows. The models are representations of past behaviors of the sequence objects. We can use the AR coef ...

In recent years modern methods of optimization have contributed greatly to the advances in data mining and related areas. These contributions continue today and promise to further advance the state of the art both in terms of modeling innovations and new solution methodologies. In this paper, we present a new modeling and solution methodology for unsupervised clustering. Preliminary computation...

Our regulatory institutions, broadly taken, include our moral norms and models and have not fully adapted to significant changes in data mining technology. For example, we suggest that the metaphors — Big Brother and “data mining” itself — commonly used to describe and assess this new technology are deficient, overemphasizing social discipline by the state and the passivity of the so-called dat...

Final task for the course Data Mining, BISS 2006, prof.sa Rosa Meo. 1 Stochastic Finite State Automata (SFSA) In this section we analyse the Hidden Markov Models (HMM) as part of a larger theory, the automata theory, as suggested in [2]. This allows us to show on one hand the relations between these models and others which are well-known pointing out similar and different aspects, on the other ...

In recent years, improvement in ubiquitous technologies and sensor networks have motivated the application of data mining techniques to network organized data. Network data describe entities represented by nodes, which may be connected with (related to) each other by edges. Many network datasets are characterized by a form of autocorrelation where the value of a variable at a given node depends...

Data Mining, also called Knowledge Discovery in Databases, is a young area of research, which has emerged in response to the flood of data we are faced with nowadays. It has taken up the challenge to develop techniques that can help humans discover useful patterns in their data. One such technique—which certainly is among the most important, as it can be used for frequent data mining tasks like...

Following World War II, a great need for higher education institutions arose in the United States, and the higher education leaders built institutions on “build it and they will come” basis. After the World War II, enrollment in the public as well as the private institutions soared (Greenberg, 2004); however, this changed by 1990s, due to a significant drop in enrollment, universities were in a...