Discretization Based on Clustering Methods

نویسنده

  • Daniela Joiţa
چکیده

Many data mining algorithms require as a pre-processing step the discretization of real-valued data. In this paper we review some discretization methods based on clustering. We describe in detail the algorithms of discretization of a continuos real-valued attribute using the hierarchical graph clustering methods.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Discretization Numerical Data for Relational Data with One-to-Many Relations

Problem statement: Handling numerical data stored in a relational database has been performed differently from handling those numerical data stored in a single table due to the multiple occurrences (one-to-many association) of an individual record in the non-target table and non-determinate relations between tables. Numbers in Multi-Relational Data Mining (MRDM) were often discretized, after co...

متن کامل

An Evolutionary Multi-objective Discretization based on Normalized Cut

Learning models and related results depend on the quality of the input data. If raw data is not properly cleaned and structured, the results are tending to be incorrect. Therefore, discretization as one of the preprocessing techniques plays an important role in learning processes. The most important challenge in the discretization process is to reduce the number of features’ values. This operat...

متن کامل

Using a New Method to Incorporate the Load Uncertainty into the SEP Problem

In this paper, a new method is conducted for incorporating the forecasted load uncertainty into the Substation Expansion Planning (SEP) problem. This method is based on the fuzzy clustering, where the location and value of each forecasted load center is modeled by employing the probability density function according to the percentage of uncertainty. After discretization of these functions, the ...

متن کامل

Classification of Efficient Imputation Method for Analyzing Missing Values

In Statistical analysis, missing data is a common problem for data quality. Many real datasets have missing data. Imputation preserves all cases by replacing missing data with a probable value based on other available information. Once all missing values have been imputed, the data set can be analyzed using standard techniques for complete data. This paper aim is to describe the efficient imput...

متن کامل

European and American put valuation via a high-order semi-discretization scheme

Put options are commonly used in the stock market to protect against the decline of the price of a stock below a specified price. On the other hand, finite difference approach is a well-known and well-resulted numerical scheme for financial differential equations. As such in this work, a new spatial discretization based on finite difference semi-discretization procedure with high order of accur...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2010