Suppose, as manager of a chain of stores, you would like to use sales transactio

Question

Suppose, as manager of a chain of stores, you would like to use sales transactional data to analyze the effectiveness of your store’s advertisements. In particular, you would like to study how specific factors influence the effectiveness of advertisements that announce a particular category of items on sale. The factors to study are: the region in which customers live, and the day-of-the-week, and time-of-the-day of the ads. Discuss how to design an efficient method to mine the transaction datasets and explain how multidimensional and multilevel mining methods can help you derive a good solution.

Explanation / Answer

Solution: -

Now-a-days efficient discovery of new items comes with huge profit deals when it comes from high utility database of a transactional database. This algorithm is being very significant over the year as this algorithm is good at producing huge dataset for the utility itemset and also decreasing the performance of the mining. It also helps in decrease in the memory consumption and the execution time.

The main objective of the mining utility is to produce high utility itemset and must produce a utility value that should be much beyond the utility values of a particular user’s threshold in a transaction. Here are the two methods which is quite effective in mining the transactional dataset:

Through these two algorithms the design can be made and the architecture will be designed.

Multidimensional mining method: -

In this method the attributes can either be quantitative or can even be categorial. So, what the quantitative attributes represent is that it incorporates hierarchy. For static discretization, the discretization must be static and also treated as categorial. In this method the data cubes are suitable for mining as they are good at increasing the mining faster. Even dynamic discretization is used as quantitative attributes.

Multilevel mining method: -

This method is good at mining data in several levels of abstraction. Moreover, this mining method is efficient at using concept hierarchy when it is used under a support confidence framework. For mining at each level, the minimum support threshold is utilized. So, the minimum support threshold in nearly 5% throughout this whole mining method. This way this method is good at providing mining solutions.

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