If you want to conduct a research project on data mining and are looking for facts and topics, then you’ve come to the right place. The previous guide 10 facts on data mining for an academic research project must have given you a comprehensive outlook on data mining and you can get further help by reading this guide which has 20 interesting topics. In fact, not only does this guide provide 20 topics, but also an essay on one them to make it easier for you to start your research work today. If you want the specifics on how to approach this academic genre then feel free to go to our guide.
Data mining is a way to sample parts of a huge amount of data. These samples, further divided into variables, can then be used in mathematical calculations and algorithms. The algorithms make it possible to predict a pattern, which can then be utilized in thousands of applications. The purpose of data mining is to find patterns and this is the ethical line that needs to be kept in check.
Here is a list of 20 topics which you can base your research project on:
Our objective is to help your train of thought get a direction so you can stop procrastinating and start working on your project. You can chose a topic from the above mentioned list or you can integrate two or more and make an even more detailed research project. There is a tsunami of information available on the internet about each and every one of the above mentioned topics so research won’t be an issue.
In data mining, association rule learning is an extremely vital tool through which two previously unrelated variables can be related in a significantly large data pool. Through this method, strong rules are successfully discovered in databases. Professor Rakesh Agrawal used the concept of strong rules to establish a different set of association rules that highlighted similarities between products even in huge amounts of transaction data in supermarkets.
If a log in the transaction data exists about a customer buying beer and potato chips, and if this is repeated by several other customers, we can safely establish the fact that the two products are connected. It is safe to assume that the next time a person buys beer, he or she will buy potato chips too. If a supermarket owner finds this out and puts the two products side by side, this assumption can turn into a fact, which will ultimately increase sales. This can also be used to design marketing campaigns. This mined data can help marketers put together two products in one picture to increase sales of both products.
Market basket analysis is an actual study which is being implemented not only in the supermarket industry but in web usage mining, continuous production, bioinformatics and intrusion detection too. Association rule learning is slightly different from sequence mining because it doesn’t take the order of items in a transaction under consideration.
Although used in many practical scenarios, association rule learning is not free of problems. One of the biggest issues with this method is that there is a significant chance of unusable or incorrect associations when an algorithm is going through massive numbers to locate items that seemed to be associated.
These incorrect associations occur by chance, as the associations between the items simply come forth due to unforeseen repetitions in the data. If the number of items is in the thousands, and the algorithm is trying to find an association between two items, then statistically speaking, there are thousands and thousands of possibilities. In this case there is the concept of statistically sound associations, which is designed to help reduce the amount of error in association though a more carefully coded probability algorithm.
There are some very famous algorithms designed over the years to create accurate association rules over the years. Although some famous algorithms exist such as Apriori, FP-Growth and Eclat, they can’t be expected to produce efficient results. In order to achieve specific and useful association results, one needs to go beyond the mining frequent item sets and create rules based on frequent item sets from a particular database.
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