Issues And Challenges In Data Mining.

hello friends, in this blog(Issues And Challenges In Data Mining) post we are going to let you know about the issues and challenges in data mining.

Data mining systems depend on the database to supply the raw input and this raises problems like databases tend to be dynamic, incomplete, noisy, and large.|Issues And Challenges In Data Mining|

Other problems arise as a result of the inadequacy and irrelevance of the information stored. The difficulties in data mining can be classified as (Issues And Challenges In Data Mining):

Limited Information
Noise or missing data
User interaction and prior knowledge
Size, updates, and irrelevant field

Limited Information:

A database is often designed for purposes other than that of data mining and, sometimes, some attributes which are essential for knowledge discovery of the application domain are not present in the data.

Thus, it may be very complex to discover significant knowledge about a given domain.

Noise and missing data:

Attributes that rely on subjective or measurement judgments can give rise to errors, such that some examples may be miscategorized.

Missing data can be treated in a number of ways-simply disregarding missing values, omitting corresponding records, inferring missing values from known values,…

… and treating missing data as a special value to be included additionally in the attribute domain. The data should be cleaned so that it is free of errors and missing data.

User interaction and prior knowledge:

An analyst is not a KDD expert, but simply a person making use of the data by means of the available KDD techniques. Since the KDD process is by definition interactive and iterative,…

… it is challenging to provide a high-performance, rapid-response environment that also assists the users in the proper selection and matching of the appropriate techniques to achieve their goals.

There need to be more human-computer interaction and less emphasis on total automation, which supports both novice and expert users.

The use of domain knowledge is important in all the steps of the KDD process. It would be easy to design a KDD tool that is both interactive and iterative.


This refers to the severity of the fault and the degree of noise in the data. Data precision is an important consideration in a discovery system.

Size, Updates, and irrelevant fields:

Databases tend to be large and dynamic, in that their contents keep changing as information is added, updated, or removed. The problem with this,…

… from the perspective of data mining, is how to ensure that the rules are up-to-date and consistent with the most current information.

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