HYBRID FUZZY RULE BASED CLASSIFICATION ALGORITHM Introduction 1.1 Purpose
The purpose of this document is to design a strategy for hybrid fuzzy rule base classification algorithm using the weka tool. This document outlines the functional requirements for hybrid fuzzy rule based classification algorithm. This document discusses the project’s goals and parameters, while giving descriptions about the potential design issues. The requirements are specified according to the finished product.
1.2 Document Conventions
This document has been written on the following style Font style Headings Sub – Headings Data Line Spacing Times New Roman 16 Bold 14 Bold 12 Regular 1.5 Lines
1.3 Intended Audience and Reading Suggestions
The document will have a wide application in which data mining solutions is required. Besides, researchers who are interested in the field of Data mining will find this system as a useful tool.
1.4 Project scope
The purpose of the project is to hybridize the underlying concepts of fuzzy rule base classification algorithm to deal with additional aspects of data imperfection. Objective of the project is to integrate different models of fuzzy rules-based classification algorithm so as to bring out a new algorithm. Goal of the project is to produce better accuracy than the other models being used for classification.
2. Overall Description
2.1 Product Perspective
Classification is a data mining algorithm that creates a step-by-step guide for how to determine the output of a new data instance. There are various models available for doing classification like FuzzyNN, FuzzyRoughNN,NN, and FuzzyOwnershipNN etc. In this research work an attempt is made to integrate these models to bring out a more efficient model that can inherit the properties of these models and provide better performance in classification process.
2.2 Product Features
The system has four modules. o o o o Creating an Instance to load input data source. Assigning Classes to rank the attribute. Applying Classification algorithm to analyze result set. Visualizing the result set.
2.3 User Classes and Characteristics
The various user classes of this project are students, research scholars and people interested in area of Data mining.
2.4 Operating Environment
Since Weka is written in pure java, it is able to run on most hardware and operating system. Naturally, the hardware requirements will vary depending on nature and size of problem, though the basic requirements to run Weka are quite small and should be abl e to run on even older hardware. Windows Environment is used for this work. 2.4.1 Hardware Specification Processor Memory Hard Disk Drive : Intel Pentium IV : 512 MB RAM : 2 GB
2.4.2 Software Specification Operating System Front End Different Data Set : Windows 7 : Weka : Machine Learning Repository
2.5 Design and Implementation Constraints
System Constraints o o o o A Java2 compliant Virtual Machine. The Java JRE / JDK. A graphics capable machine. Weka 3.7.2 version. Netbeans 6.9 version
Processing Constraints The system needs good speed i.e. a good configuration processor and RAM, since datasets of different sizes are used.
2.6 User Documentation
This product uses WEKA, which has a user manual. Only the work and modifications that has been done to weka has to be added. Manual will include product overview, complete configuration of the used system, technical details, and contact information.
2.7 Assumptions and Dependencies
Front-end (user interaction) Weka (Waikato Environment for Knowledge Analysis) is a popular suite of machine learning software written in Java, developed at the University of Waikato, New Zealand. Weka is free software available under the GNU General Public License. Dependencies: Weka and different Algorithm/methods used in Weka. specific hardware and Software constraints. No
3. System Features
This section describes in...
Please join StudyMode to read the full document