1. Introduction

What are the "potential" potential customers? What will be the main specifications of our new product? These are the types of questions every company is looking to answer in the present.

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To answer these questions To find such answers, businesses rush to gather and store the information. In addition, the tagline of Nielson: "What people watch, listen to, or purchase," collects every type of information. This may be the case it doesn't necessarily mean that there is more information.

As you can see, there are both types of data make up the digital world. In this case the process of extracting information from massive data could contribute to making decisions. Data mining tools to identify trends and allow for knowledge-driven decisions with this in the back of our minds.

In this video we'll explore this tool. WEKA Data Mining tool.

2. Data Mining

Data mining is the process that identifies patterns and patterns within large data sets in order to predict outcomes. The results reveal patterns, common themes or patterns within the data.

For instance, a supermarket's proprietor wanted to understand about the things that are commonly bought together. After looking at transactions of customers for a few weeks He discovered:

  • The sale of bread unexpectedly has increased 75 percent when a buyer buys milk

  • 60% of people like to buy eggs with bread and milk

In all things considered, the store owner should ensure that the shop has sufficient products at the correct moment and at the right place to boost the amount of money earned.

Data mining can help companies uncover the essential information they require.

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2.1. Data Mining Process

The process of data mining comprises various stages. The first step is data acquisition. cleaning, and integration take place. In the second step, since different datasets are derived from different sources, it's important to eliminate any inconsistencies, and then ensure that all data is aligned.

Then, the selection of suitable attributes takes place. In general, data contain numerous insignificant dimensions and attributes. Therefore, selecting the appropriate attributes and reduction in dimension is crucial to get high-quality results.

Then , the selection of the most suitable algorithm to solve the underline problem. There are algorithms that are specifically designed for every type of problem. Therefore, it is essential to identify if the problem is either a clustering or classification issue precisely.

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Then, the patterns and rules created by the algorithm for data mining are used to determine useful information.


WEKA is a workbench which includes machine learning algorithms to perform data mining tasks. In general, the tasks range in scope from preparation of data to visualization, and also from clustering to classification. While WEKA's strengths lie in its ability to classify it is also able to do regression, clustering, as well as the mining of rules for association with efficiency.

It is an open-source toolkit that is available through the GNU General Public License.

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