Future trends in data mining

This article talks about the future applications of data mining and the process of data gathering and analysis. The authors said that data mining had become one of the most useful and popular concept from computer science that have gained a number of applications in real life settings. Although the article greatly deals with the technical and computing side of data mining, the article is able to give a clear description of what data mining is, how it started and the future applications of the process.
I found the article difficult to read, since my primary orientation had been on the business and management side, but then data mining is simply the gathering and processing of data and information which can be used to make decisions and major business strategies. The article has information that have been taken up in class, but the rest was alien to me, I did not know that data mining could also be used in health and medical sciences and a host of other fields. There are also a number of different ways in which data can be mined, although the course material have made mention of it, this article was really more informative.

The authors say that the future of data mining will be complex and exciting, which means that the kind of data storage and retrieval could get more complex and difficult to manage. They recommend that data mining specialists should be aware of such developments, I wonder though if this has been happening in organizations that uses data mining. Data mining without doubt is an important resource, but being able to make use of the information derived from data mining effectively needs a more precise and reliable way of gathering data, selecting which data to use and how to analyze the data.
Domingos, P. (2007). Toward knowledge rich data mining. Data Mining Knowledge Discovery, 15, 21-28.
This article argues that data mining is knowledge poor and that although data mining have been around for a long time and that it has been used in a number of fields and applications, it still is knowledge poor. The author says that the information used from data mining has not been maximized and that it remains to be poorly utilized despite the fact that it has many uses. The author also argued that data mining have become very expensive especially that pre-processing the data has become very expensive.
It is also a fact that the effectiveness of the mined information is only made possible by the ability of the organization to determine how to use the information and the data. He proposes that knowledge rich information can be achieved by the organization if it follows a number of steps that would ensure data mining information is effectively used. Knowledge-rich information should be the goal of all organizations and the author suggests how it could be achieved. The article was easy to read, he was able to make use of real-life examples that made the application of data mining more real and more important.
The course materials and readings have also stressed the need for knowledge-rich data mining results but it did not specifically say how it could be done, this article gave concrete steps on how it should be done. I also felt interested about the information in the article since it was more closely related to our course materials. This is an important read for me as it supplemented my learning in the class. I would recommend it to anyone in my class and to others who might want to learn about it.
In summary the two articles have furthered my learning on data mining. I have learned that data mining has been the brain child of computer science and from the desks of technical and mathematical minds have come a very useful technique in making use of the various information that are gathered by different businesses and institutions. In data mining, business organizations have found a scientific tool which aids in decision making, in developing business strategies and marketing strategies.
The wealth of information that can be used to determine the next products and market trends have really become the trend in industries and sets apart the major players from the less successful ones. On the other hand, data mining has its own weaknesses, and this is raised by the second article, in that it needs to be knowledge rich. This is the most important point raised by the article and steps to make data mining knowledge rich also enriches my learning.

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