Data Mining Disadvantages

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sales data warehouse include Rupees sold (sales amount in rupees), units sold (number of units sold), and amount budgeted. The fact table contains the names of the facts, or measures, as well as keys to each of the related dimension tables. 2.2.2 Primitives of Data Mining: The users of data mining systems can communicate with it by using a set of data mining primitives designed in order to facilitate efficient and fruitful knowledge discovery. Such primitives include the specification of the portions of the database or the set of data in which the user is interested, the kinds of knowledge to be mined, background knowledge useful in guiding the discovery process, interestingness measures for pattern evaluation, and how the discovered knowledge…show more content…
Data mining is an interdisciplinary field, the confluence of a set of disciplines, including database systems, statistics, machine learning, visualization, and information science. Moreover, depending on the data mining approach used, techniques from other disciplines may be applied, such as neural networks, fuzzy and/or rough set theory, knowledge representation, inductive logic programming, or high performance…show more content…
A comprehensive data mining system usually provides multiple and/or integrated data mining functionalities. Moreover, data mining systems can also be distinguished based on the granularity or levels of abstraction of the knowledge mined, including generalized knowledge (at a high level of abstraction), primitive-level knowledge (at a raw data level), or knowledge at multiple levels (considering several levels of abstraction). An advanced data mining system should facilitate the discovery of knowledge at multiple levels of abstraction. Based on the type of knowledge that is mined, data mining can be mainly classified into the following categories. 1) Association rule mining uncovers interesting correlation patterns among a large set of data items by showing attribute- value conditions that occur together frequently.

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