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What are the challenges of data mining?

Affan Malik

Grokking Modern System Design Interview for Engineers & Managers

Ace your System Design Interview and take your career to the next level. Learn to handle the design of applications like Netflix, Quora, Facebook, Uber, and many more in a 45-min interview. Learn the RESHADED framework for architecting web-scale applications by determining requirements, constraints, and assumptions before diving into a step-by-step design process.

Overview

Data mining is becoming a critical technology for companies and researchers in many disciplines. Although data mining is very powerful, it faces many challenges during implementation.

Challenges can be related to performance, data, methods, techniques, etcetera. The data mining process is successful when issues are appropriately identified and organized.

An illustration of challenges in data mining

Challenges of data mining

Data mining presents a variety of challenges, including the following:

  • Distributed data is typically stored on different platforms in a distributed computing environment. It's very difficult to combine all the data into one central data store, mainly for organizational and technical reasons. For example, offices in different regions can have their servers for storing data, but not all data (millions of terabytes) from all offices can be stored on a central server.
  • Safety and social challenges arise during decision-making strategies. These strategies are implemented by sharing data collection, which requires essential security. Personal and sensitive information about individuals is collected for customer profiling and understanding user behavior patterns. Unauthorized access and confidentiality of information are becoming critical issues.
  • Complex data is heterogeneous and can be in images, audio, video, spatial dataIt is the type of data that references a geographical area, time seriesIt is a type of data points indexed in time order, natural language text, and more. It is challenging to process these different data types and extract the necessary information. It will likely need to develop new tools and methods to extract relevant information.
  • Performance of a data mining system depends primarily on the efficiency of the algorithms and techniques used. In particular, the execution time of data mining algorithms in large databases should be predictable and acceptable. Algorithms with exponential or polynomial complexity cannot be used efficiently.
  • Background knowledge incorporation can be integrated, and more accurate and dependable data mining solutions may be discovered. Predictive tasks can produce more accurate forecasts, but descriptive studies can make more beneficial results. Regardless, obtaining and incorporating essential information is an unpredictable cycle.

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Affan Malik
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Grokking Modern System Design Interview for Engineers & Managers

Ace your System Design Interview and take your career to the next level. Learn to handle the design of applications like Netflix, Quora, Facebook, Uber, and many more in a 45-min interview. Learn the RESHADED framework for architecting web-scale applications by determining requirements, constraints, and assumptions before diving into a step-by-step design process.

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