Data Mining, a relatively promising technology devised by human. Data mining defined as the process of unveiling hidden facts by simply analyzing enormous data, the data is then stored in a database or a data warehouse. By simply using these data’s it results to the creation of artificial intelligence, machine learning and statistical statements.
Many industries are now utilizing the advantage of using data mining, these includes, marketing, aerospace, manufacturing, chemical… etc, for them to increase the efficiency of their business. Therefore there is a need of data mining process.
The process in data mining should be reliable and dependable by businesses even with those who have no background or little knowledge with data mining.
The first thing to do is to understand the starting point, this is the data collection. The data is collected from sources, to make us familiar with data.
Some significant activities should be executed including the data load and the integration of the data to ensure a successful collection of data.
The next thing in it is “gross”, this is the acquired data that needs to be examined and reported. After examining, the data will then be explored using the general principles of data mining, this involves visualization, querying and reporting.
Finally after getting the result, the quality of the data should be examined by using important queries and to check if there are missing values during the collection of the data.
Preparation of data
The preparation of data normally consumes 90 percent of time in the whole duration of the planned project.
The preparation of the data should be the final set of data. Once the data source becomes available and identified, they will undergo election, cleaning, construction and will be formatted in its form.
The exploration of the data is task in exploring dept examination that will be carried during the process so the patterns will be discovered for better understanding of the business.
The techniques in modeling should be selected based on the dataset that is being prepared, then the scenario is generated in order to authenticate the validity and quality of the desired model.
Then another model is created by using a tool for the data set. And lastly, the chosen model should be carefully assessed to make sure that these models met the business initiatives.
In this phase, the result should be evaluated in connection with the business goals and objectives formulated from the start. This phase might create new requirement from business based from the patterns discovered during the evaluation.
Understanding data mining is an iterative process, the decision to go or no should be made for you to move on the deployment stage.
This is now the result, the new information or knowledge gain from data mining process. The brand new data will be presented so that stakeholders can make use of it as needed.
Based from the guidelines, the phase of deployment should be simple since the new data now will be utilized.