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Major Issues in Data Mining
2021-01-16 09:54:43

Major Issues in Data Mining

Major Issues in Data Mining: Data Mining is not very simple to understand and implement. As it is already evident that Data Mining is a process which is very crucial for various researchers and businesses. But in data mining, the algorithms are very complex and on top of that, the data is not readily available at one place. Every technology has flaws or issues. But one needs to always know the various flaws or issues that technology has.

In this article, the issues faced generally have been discussed in detail. There is a diagram given displays major issues in data mining:

Major Issues in Data Mining

Mining Methodology and User Interaction Issues:

  1. Mining different kinds of knowledge in databases: This issue is responsible for addressing the problems of covering a big range of data in order to meet the needs of the client or the customer. Due to the different information or a different way, it becomes difficult for a user to cover a big range of knowledge discovery task.
  1. Interactive mining of knowledge at multiple levels of abstraction: Interactive mining is very crucial because it permits the user to focus the search for patterns, providing and refining data mining requests based on the results that were returned. In simpler words, it allows user to focus the search on patterns from various different angles.
  1. Incorporation of background of knowledge: The main work of background knowledge is to continue the process of discovery and indicate the patterns or trends that were seen in the process. Background knowledge can also be used to express the patterns or trends observed in brief and precise terms. It can also be represented at different levels of abstraction.
  1. Data mining query languages and ad hoc data mining: Data Mining Query language is responsible for giving access to the user such that it describes ad hoc mining tasks as well and it needs to be integrated with a data warehouse query language.
  • Presentation and visualization of data mining results: In this issue, the patterns or trends that are discovered are to be rendered in high level languages and visual representations. The representation has to be written so that it is simply understood by everyone.
  • Handling noisy or incomplete data: For this process, the data cleaning methods are used. It is a convenient way of handling the noise and the incomplete objects in data mining. Without data cleaning methods, there will be no accuracy in the discovered patterns. And then these patterns will be poor in quality.

Performance Issues:

It has been noticed several times before also that there are performance related issues in data mining as well. These issues are listed as follows:

Efficiency and Scalability of data mining algorithm: Efficiency and Scalability is very important when it comes to data mining process. It is also very necessary because with the help of using this, the user can withdraw the information from the data in a more effective and productive manner.

On top of that, the user can withdraw that information effectively from the large amount of data in various databases.

Parallel, distributed and incremental mining algorithm: There are a lot factors which can be responsible for the development of parallel and distributed algorithms in data mining.

These factors are large in size of database, huge distribution of data, and data mining method that are complex.

In this process, the first and foremost step, the algorithm divides the data from database into various partition. In the next step, that data is processed such that it is situated in parallel manner. Then the last step, the result from the partition is merged.

Diverse Data Types Issues:

The issues in this type of issue are given below:

Handling of relational and complex types of data: The database may contain the various data objects for example, complex, multimedia, temporal data, or spatial data objects. It is very difficult to mine all these data with the help of a single system.

Mining information from heterogeneous databases and global information systems: The problem in this kind of issue is to mine the knowledge from various data sources. These data are not available as a single source instead these data are available at the different data sources on LAN or WAN. The structures of these data are different as well.