Data Miner package

Introduction

Download the Data miner evaluation version Screen shot of the demo Classification PDF

Description

Data Miner is a set of components for classification for Borland Delphi, written in 100% Visual Component Library (VCL). The algorithms included cover: k-nearest neighbor (KNN), and Naive Bayes, plus a third completely new Linear Classifier algorithm. The algorithms can work on real and discrete data and can be connected to a TDataSet descendant. They appropriately handle missing data and are all capable of incremental learning. The package includes a demo with reference results on standard domains, examples of usage and performance tests. The package also contains a 9-page document giving the user a rush introduction to all the key characteristics of classification algorithms. The classification algorithms of Data Miner are used to tackle the same kind of problems for which neural networks are used.

Changes v1.1 (April 2005)

• Fixed streaming of component states. Now all learned data can be saved and loaded from the disk.
• Added cosine distance measurement for KNN algorithm.

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