AUTOMATED MARKET BASKET ANALYSIS SYSTEM. A RESEARCH PROJECT MATERIAL ON COMPUTER SCIENCE EDUCATION
Data mining is described as the extraction of hidden helpful information from a collection of huge databases, data mining is also a technique that encompasses an enormous form of applied mathematics and compultational techniques like link analysis,clustering, classification, summarizing knowledge , regression analysis and so on. data mining tools predict future trends and behaviors, permitting businesses to create knowledge-driven selections. The machine-driven, prospective analyses offered by data mining move on the far side the analyses of past events. data mining tools provides answer to business questions that were time consuming. They search databases for hidden patterns, finding useful information that is beyond the reach of specialists.
Data mining techniques is enforced speedily on existing package and hardware platforms to reinforce the worth of existing information resources, and might be integrated with new product and systems as they’re brought. once enforced on high performance client/server or multiprocessing computers, data mining tools will analyze huge databases to provide answers to questions such as, ”What goods consumers tend to buy the most and goods that go along side with it”.
Coenen(2010) in his publication” Data Mining: Past, Present and Future” discussed the history of data mining can be dated as far back as late 80s when the term began to be used, at
least within the research community and diffrentiated it from sql.
Broadly data mining can be defined as as set of mechanisms and techniques, realised in software,
to extract hidden information from data. However,the word hidden in this definition is important;
By the early 1990s data mining was commonly recognised as a sub process within a larger process called Knowledge Discovery in Databases or KDD , the most commonly used definition of KDD is that of Fayyad et al as “the nontrivial process of identifying valid, novel, potentially useful and ultimately understandable patterns in data.’’ (Fayyad et al. 1996).
As such data mining should be viewed as the sub-process, within the overall KDD process, concerned with the discovery of hidden information”. Other sub-processes that form part of the KDD process are data preparation (warehousing, data cleaning, pre-processing,and so on) and the analysis/visualisation of results. For may practical purposes KDD and data mining are seen as synonymous, but technically one is a sub-process of the other. The data that data mining techniques were originally directed at was tabular data and, given the processing power available at the time, computational eficiency was of significant concern. As the amount of processing power generally available increased, processing became less of a concern and was replaced with a desire for accuracy and a desire to mine ever larger data collections. Today, in the context of tabular data, we have a well established range of data mining techniques available.
It is well within the capabilities of many commercial enterprises and researchers to mine tabular
data, using software such as Weka, on standard desktop machines. However, the amount of electronic data collected by all kinds of institutions and commercial enterprises, year on year, continues to grow and thus there is still a need for efective mechanisms to mine ever larger data sets. The popularity of data mining increased significantly in the 1990s, notably with the establishment of a number of dedicated conferences; the ACM SIGKDD(special intrest group on knowledge discovery in data) annual conference in 1995, and the European PKDD(practice of knowledge discovery in databases) and the Pacific/Asia PAKDD(pacific asiaconference on knowledge discovery and data mining) conferences This increase in popularity can be attributed to advances in technology; the computer processing power and data storage capabilities available meant that the processing of large volumes of data using desktop machines was a realistic possibility. It became common place for commercial enterprises to maintain data in computer readable form, in most cases this was primarily to support commercial activities, the idea that this data could be mined often came second.