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Data Mining Issues

Here, we are going to learn about the Data Mining Issues, most common challenges, etc.
Submitted by Palkesh Jain, on February 09, 2021

Today, data mining is in great demand as it helps companies to provide insights and study how their product sales can increase. Data mining has great strengths and is a competitive and fast-expanding industry. For example, a fashion shop that registers any of its customers who purchase a product from their store. Based on customer data such as age, gender, income group, occupation, etc., the store would be able to figure out what kind of consumers buy different products. In this example, the customer's name is of no value so we can not forecast the buying pattern by name as to whether or not that consumer will buy a certain product. The age group, ethnicity, income group, occupation, etc. will therefore be used to locate valuable details. "Data Mining" is looking for facts or fascinating trends in data.

Data mining applications in today's world face a number of difficulties and problems; many of these problems have been resolved to a certain degree in recent research and development of data mining and are now considered criteria for data mining; some are still at the research level.

Some of the most common challenges are described below -

1) Mining Methodology

New mining tasks continue to evolve as there are diverse applications. These activities may use the same database in numerous ways and require new techniques for data mining to be created. We need to traverse multidimensional space when looking for information in big datasets. Various variations of measurements need to be implemented to identify fascinating patterns. Uncertain, chaotic and imperfect data may also lead to incorrect derivation.

2) User Interaction Issue

The method of processing data can be extremely immersive. It is important to be user-interactive in order to facilitate the mining process. In the course of data mining, all domain information, context knowledge, limitations, etc. should be combined. The knowledge uncovered by data mining should be accessible to humans. An expressive representation of information, user-friendly simulation techniques, etc., should be implemented by the framework.

3) Performance and Scalability

In order to efficiently retrieve interesting data from a large volume of data in data warehouses, data mining algorithms should be robust and scalable.
The development of parallel and distributed data-intensive algorithms is inspired by the large distribution of data and computational complexity.

The data mining algorithm must be efficient and scalable in order to efficiently extract information from huge amounts of data in databases. The enormous size of many databases, the wide distribution of data, and the complexity of some data mining methods are factors that motivate the development of algorithms for parallel and distributed data mining. These algorithms split the data into partitions that are analyzed simultaneously.

4) Data type diversity

Handling with relational and dynamic data types: In libraries and data warehouses, there are various kinds of data stored. Both these types of data cannot be extracted by one machine. Along with different types of data, data mining solutions should be built. Mining data from heterogeneous datasets and global information systems: Because data is collected from numerous Local Area Network (LAN) and Wide Area Network (WAN) data sources, the discovery of information from various organized sources is a major challenge for data mining.

5) Data mining and society

The areas of interest that need to be discussed are the disclosure of the use of data and the potential infringement of human privacy and protection of rights. Application of data mining found unique data mining solutions, tools for the environment, intelligent addressing of questions, monitoring of processes, and decision making.

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