Waikato Environment for Knowledge Analysis - Essay Example

Weka Programme Introduction Weka Programme is a suite for machine learning software which is a collection of algorithms written in Java and used for data mining tasks. These algorithms can be applied either directly to the dataset or through the Java code. The software contains many tools including that for data pre-processing, classification, clustering, regression, association rules, and visualization. It can also be used for the development of new machine learning schemes. This software was found by the University of Waikato on the islands of New Zealand. It was named after a bird which had an inquisitive nature. It is a free software issues under GNU General Public License. Weka stands for Waikato Environment for Knowledge Analysis. It is the software which allows businesses to extract useful information out of large databases. This is the process of data mining which is a field that analyzes large sets of data and discovers patterns and methods for the management, processing, and inference considerations of the data. Weka software offers businesses a collection of learning tools and schemes that may be used for data mining (Witten, 2011, p. 132). There are four applications in the program which are accessible; these are Explorer, Experimenter, KnowledgeFlow, and Simple CLI. The first section of the software is to open the database or the dataset and make the edits as the user wishes. These edits include the filtering of data contents, changing of attributes, and visualizing the result in a bar chart. The available data can also be classified according to the set of rules which are predefined and can perform a complete analysis of cost-benefit that would display automatically the threshold curve and the cost matrix (Witten, 2011, p. 132). Moreover, the program also has many tools for the data clustering, attributes evaluator, and association rules. In addition, businesses also use it for the data plotting allowing the user to view and analyze the point graphs individually for each possible attribute combination. Businesses also use the weka programme as it is suitable for the development of new machine learning schemes. The user simply has to configure the experiment by choosing the type: classification or regression. Then the desired data set and algorithm has to be chosen and then it is ready to be run. The results of this procedure can be saved in CSV format or ARFF or as JDBC database. A data file can also be analyzed or tested through the program as it allows the user to choose the comparison field and the sorting criteria on which the test is based. Weka is used in businesses as it easy to use software designed for those familiar with procedures of data mining and database analysis (Hill, 2012, p. 538). Data mining is the most common thing used in the tech industry as the businesses are generating thousands of data points for their users and looking for ways to turn the collected information into increased revenues. Data mining is used as a collective term for several techniques that combine and function to turn data from raw information into something meaningful. Models and rules are created in the application of data mining which are helpful to predict the future behaviour, improve the business, and explain things which are not understandable initially (Witten, 2011, p. 132). The process also helps in finding things that are new and that the user never knew of. Weka provides software for data mining which is usable for an average person who would start data mining. There are other commercial software products as well but it is very difficult to actually be an expert in data mining. Weka provides an easier approach and guides the user through the initial problems. Weka provides an open source for the business to mine their own data and find out about the clients and apply the results to improve the performance of the business (Witten, 2011, p. 132). When using the weka programme, the user has an option to apply different algorithms into the data set to produce a representation of the information in order to make the observation easier. Identifying the suitable options which would provide the best output may be difficult. However, the best way to approach is to apply a combination of the available choices independently and see what produces something closest to the results desired (Hill, 2012, p. 538). The classify tab is the category which gives several options that could be selected. There is an option for test which gives choices to user for four different test mode scenarios to be used on the dataset. These four modes are: use training set, supplied training set, cross validation, and split percentage (Kaluza, 2013, p. 28). The classification in weka has been developed in order to train the data set for the production of the output that has been classified on the basis of the last attribute in the dataset’s characteristics. In order to use a specific attribute, the user must select the option from the options menu prior to the testing (Kaluza, 2013, p. 28). The cluster tab is used to open the process which identifies commonalties or clusters within the dataset and produces information that the user can analyze. Some of the options in the cluster window are similar to those in the classifier tab (Kaluza, 2013, p. 28). The cluster tab provides an option for cluster evaluation which compares the data with a pre-assigned data class. In cluster mode, the users have an option to ignore some of the dataset attributes. This is useful when there are specific attributes that cause results to get out of range (Kaluza, 2013, p. 28). Selecting specific attributes is used for the process of calculation. In the default of the software, all the attributes that are available are used for evaluation. If the user wants to exclude some attributes they will have to deselect those choices from the list (Kaluza, 2013, p. 28). Some attributes are in different forms as the alphanumeric data. For the desired calculation, the software automatically selected the attributes and decides which ones will fit best (Hill, 2012, p. 538). Thus, many businesses in the tech industry have started using this software as it is a simple one for beginners and helps in the generation of revenues of business through data mining. References Hill, T. 2012. Practical Text Mining and Statistical Analysis for Non-Structured Text Data Applications. London: Academic Press Kaluza, B. 2013. Instand Weka How-to. London: Packt Publishing Ltd Witten, I. 2011. Data Mining: Practical Machine Learning Tools and Techniques. NY: Elsevier Read More