Major Issue In Data Mining
Major Issues In Data Mining:
In this Article learn Mining major issues in data mining. the previous tutorial learns Data mining process follow this link: Data mining process
kinds of knowledge in databases. – :
The need for different users is
not the same. And Different users may be in interested in different kinds of
knowledge. Therefore it is necessary for data mining to cover a broad range of
knowledge discovery tasks.
knowledge at multiple levels of abstraction. –:
The data mining process needs to be
interactive because it allows users to focus the search for patterns, providing
and refining data mining requests based on returned results.
Incorporation of
background knowledge. –
To guide the discovery process and
to express the discovered patterns, the background knowledge can be used.
Background knowledge may be used to express the discovered patterns not only in
concise terms but at multiple levels of
Data mining query languages and ad hoc data mining. –:
Data Mining Query language
that allows the user to describe ad hoc mining tasks, should be integrated with
a data warehouse query language and optimized for efficient and flexible data
mining.
Presentation and
visualization of data mining results. – :
Once the patterns are
discovered it needs to be expressed in high-level languages, visual
representations. This representation should be easily understandable by the
users. Handling noisy or
incomplete data. – :
The data cleaning methods are
required that can handle the noise, incomplete objects while mining the data
regularities. If data cleaning methods are not there then the accuracy of the
discovered patterns will be poor.
e Pattern evaluation.
–
It refers to the interestingness
of the problem. The patterns discovered should be interesting because either
they represent common knowledge or lack novelty
• Efficiency and
scalability of data mining algorithms. - In order to effectively extract the information from a huge amount of data in databases,
data mining algorithms must be efficient and scalable Parallel, distributed, and incremental mining algorithms. - The factors such as the huge size of databases, a wide distribution of data and complexity of data mining methods.
motivate the development of parallel and distributed data mining algorithms. These algorithms divide the data into partitions that are further processed parallel. Then the results from the partitions are merged. The incremental algorithms, updates databases without having mined the data again from scratch learn today major issues in data mining.
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