What are the Major Issues and Challenges of Data Mining?
Though data mining is very powerful, it faces many challenges during its implementation. The issues and challenges of Data Mining could be related to performance, data, methods and techniques used etc. The data mining process becomes successful when the challenges or issues are identified correctly and sorted out properly.
Major Issues and Challenges of Data Mining
There are some Issues of Data Mining are as follow:
1. Mining Methodology
- Mining various and new kinds of knowledge
- Mining knowledge in multi-dimensional space
- Data mining: An interdisciplinary effort
- Boosting the power of discovery in a networked environment
- Handling noise, uncertainty, and incompleteness of data
- Pattern evaluation and pattern- or constraint-guided mining
2. User Interaction
- Interactive mining
- Incorporation of background knowledge
- Presentation and visualization of data mining result
3. Efficiency and Scalability
- Efficiency and scalability of data mining algorithms
- Parallel, distributed, stream, and incremental mining methods
4. Diversity of data types
- Handling complex types of data
- Mining dynamic, networked, and global data repositories
5. Data mining and society
- Social impacts of data mining
- Privacy-preserving data mining
- Invisible data mining
Challenges of Data Mining
There are some Challenges of Data Mining are as follow:
1. Noisy and Incomplete Data
Data mining is the process of extracting information from large volumes of data. The real-world data is heterogeneous, incomplete and noisy. Data in large quantities normally will be inaccurate or unreliable. These problems could be due to errors of the instruments that measure the data or because of human errors.
Suppose a retail chain collects the email id of customers who spend more than $200 and the billing staff enters the details into their system. The person might make spelling mistakes while entering the email id which results in incorrect data.
Even some customers might not be ready to disclose their email id which results in incomplete data. The data even could get altered due to system or human errors. All these result in noisy and incomplete data which makes the data mining really challenging.
2. Distributed Data
Real world data is usually stored on different platforms in distributed computing environments. It could be in databases, individual systems, or even on the Internet. It is practically very difficult to bring all the data to a centralized data repository mainly due to organizational and technical reasons.
For example, different regional offices might be having their own servers to store their data whereas it will not be feasible to store all the data (millions of terabytes) from all the offices in a central server.
So, data mining demands the development of tools and algorithms that enable mining of distributed data.
3. Complex Data
Real world data is really heterogeneous and it could be multimedia data including images, audio and video, complex data, temporal data, spatial data, time series, natural language text and so on.
It is really difficult to handle these different kinds of data and extract required information. Most of the times, new tools and methodologies would have to be developed to extract relevant information.
The performance of the data mining system mainly depends on the efficiency of algorithms and techniques used. If the algorithms and techniques designed are not up to the mark, then it will affect the performance of the data mining process adversely.
5. Incorporation of Background Knowledge
If background knowledge can be incorporated, more reliable and accurate data mining solutions can be found. Descriptive tasks can come up with more useful findings and predictive tasks can make more accurate predictions. But collecting and incorporating background knowledge is a complex process.
6. Data Visualization
Data visualization is a very importance process in data mining because it is the main process that displays the output in a presentable manner to the user. The information extracted should convey the exact meaning of what it actually intends to convey.
But many times, it is really difficult to represent the information in an accurate and easy-to-understand way to the end user. The input data and output information being really complex, very effective and successful data visualization techniques need to be applied to make it successful.
7. Data Privacy and Security
Data mining normally leads to serious issues in terms of data security, privacy and governance.
For example, when a retailer analyzes the purchase details, it reveals information about buying habits and preferences of customers without their permission.