Major Issues In Data Mining-Purpose & Challenges

Data mining (one of the core disciplines of data science) helps in data extraction but there are many difficulties in data mining. More difficulties get uncovered as the genuine data mining measure begins, and the achievement of data mining lies in defeating every one of these difficulties. Different data mining instruments operate in distinct ways due to the different algorithms used in their design. Therefore, the selection of the right data mining tools is a very challenging task. In this article, we will understand the purpose of data mining and the various issues that take place with it.

What is data mining?

The process of extracting information to identify patterns, trends, and useful data that would allow the business to take data-driven decisions from huge sets of data is called data mining. In the context of computer science, Data Mining can be referred to as knowledge mining from data, knowledge extraction, data/pattern analysis, data archaeology, and data dredging.

Purposes of data mining 

Data mining can be applied to any type of data e.g. Data Warehouses, Transactional Databases, Relational Databases, Multimedia Databases, Spatial Databases, Time-series Databases, and World Wide Web.

What are the major issues of data mining?

Mining methodology and user interaction issues

1. User Interface: The knowledge is determined utilizing data mining devices is valuable just in the event that it is fascinating or more all reasonable by the client. From great representation translation of data, mining results can be facilitated, and betters comprehend their prerequisites. To get a great perception, many explorations are done for enormous data sets that manipulate and display mined knowledge.
   
   
2. 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 tasks.
   
   
3. 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 users to focus the search on patterns from various different angles.
   
   
4. Incorporation of the 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.
   
   
5. 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.
   
   
6. 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.
   
   
7. Handling noisy or incomplete data: For this process, data cleaning methods are used. It is a convenient way of handling noise and 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.
   
   
8. Noisy and incomplete data: Data Mining is the way toward obtaining information from huge volumes of data. This present reality information is noisy, incomplete, and heterogeneous. Data in huge amounts regularly will be unreliable or inaccurate. These issues could be because of human mistakes blunders or errors in the instruments that measure the data.
   
   
9. Incorporation of background knowledge: In the event that background knowledge can be consolidated, more accurate and reliable data mining arrangements can be found. Predictive tasks can make more accurate predictions, while descriptive tasks can come up with more useful findings. Be that as it may, gathering and including foundation knowledge is an unpredictable cycle.

Performance issues 

1. Performance: The presentation of the data mining framework basically relies upon the productivity of techniques and algorithms utilized. On the off chance that the techniques and algorithms planned are not sufficient; at that point, it will influence the presentation of the data mining measure unfavorably.
   
   
2. Scalability and Efficiency of the Algorithms: The Data Mining algorithm should be scalable and efficient to extricate information from tremendous measures of data in the data set.
   
   
3. Parallel and incremental mining algorithm: There are a lot of factors that can be responsible for the development of parallel and distributed algorithms in data mining. These factors are the large size of the database, the huge distribution of data, and the data mining methods that are complex. In this process, the first and foremost step, the algorithm divides the data from the database into various partitions. In the next step, that data is processed such that it is situated in a parallel manner. Then the last step, the result from the partition is merged.
   
   
4. Distributed Data: True data is normally put away at various stages in distributed processing conditions. It very well may be on the internet, individual systems, or even on databases. It is essentially hard to carry all the data to a unified data archive principally because of technical and organizational reasons.
   
   
5. Managing relational as well as complex data types: Many structures of data can be complicated to manage as they may be in the form of tabular, media files, spatial and temporal data. Mining all data types in one go is tougher to do.
   
   
6. Data mining from globally present heterogeneous databases: Since databases are fetched from various data sources available on LAN and WAN. These structures can be organized and semi-organized. Thus, making them streamlined is the hardest challenge.

Also read: Importance of data transformation in data mining

Diverse data types issues

1. Security and Social Challenges: Dynamic techniques are done through data assortment sharing, so it requires impressive data security. Private information about people and touchy information is gathered for the client’s profiles, client standard of conduct understanding—illicit admittance to information and the secret idea of information turning into a significant issue.
   
   
2. Complex Data: True data is truly heterogeneous, and it very well may be media data, including natural language text, time series, spatial data, temporal data, complex data, audio or video, images, etc. It is truly hard to deal with these various types of data and concentrate on the necessary information. More often than not, new apparatuses and systems would need to be created to separate important information.
   
   
3. Improvement of Mining Algorithms: Factors, for example, the difficulty of data mining approaches, the enormous size of the database, and the entire data flow inspire the distribution and creation of parallel data mining algorithms.
   
   
4. Data Visualization: Data visualization is a vital cycle in data mining since it is the foremost interaction that shows the output in a respectable way to the client. The information extricated ought to pass on the specific significance of what it really plans to pass on. However, ordinarily, it is truly hard to address the information in a precise and straightforward manner to the end-user. The output information and input data being very effective, successful, and complex data perception methods should be applied to make it fruitful.
   
   
5. Data Privacy and Security: Data mining typically prompts significant issues regarding governance, privacy, and data security. For instance, when a retailer investigates the purchase details, it uncovers information about purchasing propensities and choices of customers without their authorization.

Examples: 

• Data Integrity: A bank may maintain credit card accounts on several different databases. The addresses (or even the names) of a single cardholder may be different in each. Software must translate data from one system to another and select the address most recently entered.
  
  
• Overfitting: Over-fitting occurs when the model does not fit future states a classification model for a student database may be developed to classify students as excellent, good, or average. If the training database is quite small, the model might erroneously indicate that an excellent student is anyone who scores more than 90% because there is only one entry in the training database under 90%. In this case, many future students would be erroneously classified as excellent. Over-fitting can arise under other circumstances as well, even though the data are not changing.
  
  
• Large data sets: The massive datasets associated with data mining create problems when applying algorithms designed for small datasets. Many modeling applications grow exponentially on the dataset size and thus are too inefficient for larger datasets. Sampling and parallelization are effective tools to attack this scalability problem.

Summing Up

In a nutshell, the following problems take place in data mining: 

1. Poor data quality such as noisy data, dirty data, missing values, inexact or incorrect values, inadequate data size, and poor representation in data sampling.
   
   
2. Integrating conflicting or redundant data from different sources and forms: multimedia files (audio, video, and images), geo data, text, social, numeric, etc.
   
   
3. The proliferation of security and privacy concerns by individuals, organizations, and governments.
   
   
4.  Unavailability of data or difficult access to data.
   
   
5. Efficiency and scalability of data mining algorithms to effectively extract the information from the huge amount of data in databases.
   
   
6. Dealing with huge datasets that require distributed approaches.
   
   
7. Dealing with non-static, unbalanced, and cost-sensitive data.
   
   
8. Mining information from heterogeneous databases and global information systems.
   
   
9. Constant updation of models to handle data velocity or new incoming data.
   
   
10. High cost of buying and maintaining powerful software, servers, and storage hardware that handle large amounts of data.
    
    
11. Processing of large, complex, and unstructured data into a structured format.
    
    
12. The sheer quantity of output from many data mining methods.

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