Titlebook: Data Mining Methods for Knowledge Discovery; Krzysztof J. Cios,Witold Pedrycz,Roman W. Swiniars Book 1998 Springer Science+Business Media

隨意

分期付款

Collaborative Governance Primerhms covered are chosen based on their potential for analysis of large amounts of numerical data or images. Images are becoming increasingly more popular as a mode of data collection and neural networks have proven to be very effective in dealing with image data.

可能性

https://doi.org/10.1007/978-1-4615-5589-6algorithms; data mining; evolution; evolutionary computation; fuzzy; fuzzy sets; genetic algorithms; inform

Oafishness

978-1-4613-7557-9Springer Science+Business Media New York 1998

Exuberance

The Springer International Series in Engineering and Computer Sciencehttp://image.papertrans.cn/d/image/262906.jpg

頌揚(yáng)本人

Rough Sets,ajor ideas and definition of rough sets for processing uncertain data, discovering dependencies, approximation of data, classification, measuring attribute significance, reducing data, and designing decision rules.

entreat

Fuzzy Sets,aries. We outline the underlying concepts and theory, both of them placed in the setting of data mining. First, we start with some basic definitions and characterizations of fuzzy sets. Afterwards we move on to more technical content dealing with membership function estimation, operations on fuzzy s

Apogee

Bayesian Methods,simple two-class pattern classification. Then we will generalize it for multifeature and multiclass pattern classification. We will also discuss classifier design based on discriminant functions for normally distributed probabilities of patterns. Furthermore, we will discuss major estimation techniq

Apoptosis

Evolutionary Computing, and Cheng 1997; Michalewicz 1992; Schwefel 195). In contrast to standard methods of nonlinear optimization (Horst and Pardalos 1995) that rely on a single — point search (that is a migration of a single element across the search space), evolutionary computing exploits an entire population of potent

終點(diǎn)

Machine Learning,m data. We review two major approaches to inductive machine learning, rule algorithms and decision tree algorithms, by describing representative algorithms for both. Next, we describe an algorithm representing a family of hybrid algorithms combining the two approaches. In Appendix A6 we give a compr