Successful Data Mining: With Dimension Reduction

Dimension Reduction for Visual Data Mining

We present a method for dimension reduction applied to visual data mining in order to reduce the user cognitive load due to the density of data to be visualized and mined. We use consensus theory to address this problem: the decision of a committee of experts (in our case existing attribute selection methods) is generally better than the decision of a single expert. We illustrate the choices op...

Dimension Reduction Algorithms for Data Mining and Visualization

Analysis of Censored Survival Data with Dimension Reduction Methods‎: Tehran Lipid and Glucose Study

 ‎Cardiovascular diseases (CVDs) are the leading cause of death worldwide‎. ‎To specify an appropriate model to determine the risk of CVD and predict survival rate‎, ‎users are required to specify a functional form which relates the outcome variables to the input ones‎. ‎In this paper‎, ‎we proposed a dimension reduction method using a general model‎, ‎which includes many widely used survival m...

Critical Dimension in Data Mining

Data mining is an increasingly important means of knowledge acquisition for many applications in diverse fields such as biology, medicine, management, engineering, etc. When tackling a large-scale problem that involves a multitude of potentially relevant factors but lacking a precise formulation or mathematical characterization to allow formal approaches to solution, the available data collecte...

Fractal Dimension for Data Mining

In this project, we introduce the concept of intrinsic “fractal” dimension of a data set and show how this can be used to aid in several data mining tasks. We are interested in answering questions about the performance of a method and also in comparing between the methods quickly. In particular, we discuss two specific problems – dimensionality reduction and vector quantization. In each of thes...