ITS632 Data Mining

Term paper

Write a research paper on one of the following Data Mining open source tools. Be sure to include a title page and a references page. Place your name on the Title page.

You must write in your own words. Content which has been copied from another source will not be accepted and will result in zero (0) points being awarded for this assignment.

You may paraphrase another source and you may directly quote another source. Material directly quoted must be contained within quotation marks

All material that is paraphrased or directly quoted must give attribution to the author through the use of in-text citations such as (Jones, 2019).

Guidelines:

5 pages maximum (includes Title page and References page)

12 point, serif font such as Times New Roman

1 inch margins

Graphics/images are limited to one instance and that instance may not exceed 20% of a page

RapidMiner

Orange

Weka

Knime

Sisense

SSDT

Apache Mahout

Oracle Data Mining

Rattle

DataMelt

SAS Data Mining

TeraData

Board

Dundas

Sample Solution

Title: An Overview of Weka as an Open Source Data Mining Tool

Introduction: Data mining is the process of extracting hidden and useful patterns from large datasets. With the advancement of technology, the size of the dataset has increased significantly, which led to the development of several data mining tools. One such open-source data mining tool is Weka (Waikato Environment for Knowledge Analysis), which was developed by the University of Waikato in New Zealand. Weka is a popular data mining tool due to its user-friendly interface, scalability, and versatility. This paper provides an overview of Weka, its key features, advantages, limitations, and how it can be used for data mining.

Overview of Weka: Weka is a comprehensive suite of machine learning algorithms for data mining tasks. It is written in Java, which makes it platform-independent and can be easily integrated with other software applications. Weka provides a graphical user interface (GUI) for data preprocessing, classification, clustering, regression, and visualization. The core components of Weka are Explorer, Experimenter, Knowledge Flow, and Simple CLI (command-line interface).

Key Features of Weka:

1. Preprocessing: Weka provides various data preprocessing techniques such as attribute selection, normalization, discretization, and outlier detection to prepare the dataset for data mining tasks.
2. Classification: Weka has a wide range of…order customized answer