Pulling out important information from big volume of data is what data mining is all about. Tools used for data mining are there primarily for the purpose of examining data from different perspectives and summarizing it to useful database library. On the other hand, in an effort to handle the growing volume of data, these tools have upgraded to become computer based applications. They are sometimes referred to as knowledge discovery tools.
As an idea, data mining existed even before and what is used as data mining tools were only manual processes. Later on, with the onset of hi-tech and fast computers, increased storage capacities and analytical software tools, automated tools were developed eventually which significantly improved the accuracy of data mining speed, analysis and at the same time, brought down the operation costs.
Such methods for data mining are used in an effort to facilitate major elements such as pull out, convert as well as load data to warehouse system, collecting and handling data in database system, allow concerned personnel to acquire the data, do data analysis as well as data presentation in format that can be interpreted quickly for better decision making. These said methods of data mining are being used to be able to explore associations, trends and correlations in stored data that are generally based on different relationships like for example clusters or logical correlations used in categorizing the data collected, associations or the simplest relationship between data, sequential patterns that is used to help in predicting a certain behavior according to the observed trends in stored data and classes which is a certain predefined group drawn out and the data within it is searched based on the groups.
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The industries that cater heavily to consumers in the financial, retail, sports, entertainment, hospitality and the likes heavily depend on such methods of data mining for them to obtain quick answers to questions and at the same time, improve their business. Due to the reason that the tools help them to study and analyze consumer’s buying patterns, it enables them to create a strategy that can be made for future sales.
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A basic example of this is restaurant; they may want to know the eating habits of their consumers on different times of the day. The data will then help them to decide on the menu to offer at different parts of the day. With data mining tools, it helps them a lot to draw out a business plan, discount plans, advertising strategy and everything in between to further boosts their operations and sales.