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What is the task of mining frequent itemsets difficult?
Data mining is the phase of discovering useful new correlations, patterns, and trends by transferring through a high amount of records saved in repositories, using pattern recognition technologies including statistical and numerical techniques. It is the analysis of factual datasets to discover unsuspected relationships and to summarize the records in novel methods that are both logical and helpful to the data owner.
It is the procedure of selection, exploration, and modeling of high quantities of information to find regularities or relations that are at first unknown to obtain clear and beneficial results for the owner of the database.
Data Mining is similar to Data Science. It is carried out by a person, in a particular situation, on a specific data set, with an objective. This phase contains several types of functions including text mining, web mining, audio and video mining, descriptive data mining, and social media mining. It is completed through software that is simple or greatly specific.
By outsourcing data mining, all the work can be done quicker with low operation costs. Specific firms can also use new technologies to save data that is impossible to find manually. There are tonnes of data available on multiple platforms, but very limited knowledge is accessible.
The major challenge is to analyze the data to extract essential data that can be used to solve an issue or for company development. There are many dynamic instruments and techniques available to mine data and discover better judgment from it.
The function of mining frequent itemset is complex because it is difficult to find a strong relation among data items at low or primitive methods of abstraction because of the sparsity of information in multidimensional space.
The strong association is found at high concept levels that can represent common sense knowledge but what can represent common sense to one user can seem new to another. Thus, it is required that data mining should provide possibilities to mine association rules at multiple levels of abstractions and pass-through simply between multiple abstraction spaces.
There are the following reasons why the mining of frequent itemsets is difficult.
The computations required to generate association rules grow exponentially with the number of items and the complexity of rules being considered.
Items are considered to be identical except for one identifying features, including the product type. It is not all problems fit this description.
The most difficult task is to determine the right set of items to use in the analysis. By generalizing the items, one can ensure that the frequencies of the items used in the analysis are about the same.
It is difficult to generate association rules when there are items that rarely occur in very few transactions.
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