Data Classification Using Weka Software – Lab Report Example

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The paper “ Data Classification Using Weka Software” is a   worthy variant of a lab report on information technology. In a general understanding, data mining or knowledge discovery is the process of providing meaning to a set of data through proper analysis. The data analyzed and given meaning may come from a different range of sources. The data may be obtained as free texts from websites or other social sites, also, the data can be structured data existing within a given structured data repository. There exist various tools for data mining, and these are the tools that are used to derive sense from a given set of data.

Data mining is a widely used technique in addressing the existing complexities in data, more so the unstructured data. Weka is one of the easiest to use, but complete data mining tools. It is a machine learning tool that allows the use of various algorithms to perform data classification and other machine learning tasks. Weka algorithms (the classifiers) can be used from other programs such as java codes, python codes among others. Nevertheless, Weka can also be used directly within the software which is tailored free under the licensing of GNU General Public License (Hall et al. , 2009).

Weka as software can perform the following machine learning tasks: Data preprocessing: These refer to the set of processes that Weka allows users to perform on the data before the actual machine learning processes. Regression calculation: Regression calculation involves the use of statistical regression equation to calculate the relations among the data presented to Weka. This is a very important process in Weka’ s machine learning as it is dependent upon by various other processes like data classification and clustering. Data classification: Data classification is simply the process of organizing (classifying) data into several categories for easy, efficient, and most effective use.

A properly designed classification algorithm makes data available for business use, in other words, it makes the data easy to retrieve and analyze. Data clustering: This is the process of putting data into several different with less attention on the inner details. Compared to classification, data clustering is a simple way of putting data into simple cluster classes. Data visualization: Data visualization, as the name suggests, is a way of having a pictorial perception of a given data set.

In many cases, data visualization is done through visual reports. The visual reports are the various statistical tools that are used to obtain trends and make observations on the data. Data association: data association refers to the relationships among data sets. These associations are important in determining the classification classes of a given data set. Weka makes use of various association rules to classify data (Hall et al. , 2009). Data DescriptionData description refers to the information about the data under study.

It is closely related to the term metadata which is data about data. In data mining and classification, data description plays a very major role in coming up with the clusters or classes in which the data belongs. Weka is keen on the data types, or the file types which it has the ability to perform any processing on. The files presented to Weka must, therefore, be in either of the supported formats. Weka supports file formats like. arff, .csv, C4.5 among others.

Weka users must then have the knowledge of data and file conversion in order to achieve the best from Weka.

Reference

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. and Witten, I.H., 2009. The WEKA data mining software: an update. ACM SIGKDD explorations newsletter, 11(1), pp.10-18.
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