What Are Data Mining Issues?

What Are Data Mining Issues?

Have you ever come across data vendors? They deliver databases which they fetch from heterogeneous ways. Today, research companies are banking on such data services. In collaboration with the data vendors, they are enjoying the pinnacle. Generally, if you talk about data mining it means diving into the pool of heterogeneous data and extracting the most suitable or relevant data that matches the requirement. This process meets it end when analysis is conducted. Further, beneficial suggestions are drawn for meeting business purposes. Strategies for deduction in expenditure and rise in revenue are commonly carved through it to accelerate productivity and thereby, revenues.

Veteran data analysts find services of data mining quite tough nut to crack. It is simpler to streamline databases if the required data is extracted from one website or agency. The data miners are to browse one, two, three and so on websites, libraries, magazines and other resources. Then only, they become able to integrate it for handing over to the data analysts for doing their part of analysis. Meanwhile, let’s check out below what problems puzzle them:

Issues with methodology of data mining and user interaction:

  1. Variant data types in databases: Many customers, many desires. Since clients want different kind of information, it is essential to do data mining in broader terms. Hence, it becomes tough to cater the vast range of data in entirety that can meet the clients’ satisfaction.
  2. Interactive mining is tough to do: Interactive data mining is quite tough to do since the data mining services provider needs to eye on the patterns of searches. Thereafter, meeting request criteria and fine-tuning databases are complex tasks.
  3. To have background knowledge is must: Background knowledge acts as timely guidance that enables the data miner to get insight of the process and their patterns. Lacking it can be disastrous. Without it, presenting it in concise format is not an easy job.
  4. Query languages and ad hoc mining: The language of data mining query language should be in perfectly matched with the query language of data warehouse.The former lets the user to elaborate ad hoc tasks. If there is any pothole in such data mining practice, optimization will be impossible. As a result, efficiency along with flexibility will be impossible to introduce.
  5. Making expression of data mining result understandable: It will be no less than a tussle for the mining of data companies to represent their data mining report in simple and understandable manner. It should be framed in attractive visualization which is challenging task.
  6. Handling incomplete data:   During data cleansing process, incomplete data can be the biggest barrier to accomplish your target of discovering patterns. As a result, poor observation and thereby, inaccurate analysis will not bring satisfaction to the clients.
  7. Evaluation of the patterns: If the pattern data research companies will not be influential and acknowledgeable, it may lack its impact. Therefore, wrong interpretation or even, underestimation can be occurred.

Issues with performance

  1. Data mining algorithm’s efficiency and scalability: In case, data mining algorithm lacks efficiency and scalability, wrong conclusion can be drawn at the end. Thus, extracted information will deliver negative or no benefits at the end.
  2. Parallel, distributed and incremental mining algorithm: Certain factors, like big data bases, wider data distribution and complicated data mining structure, lead to creation of parallel and distributed data mining algorithms. Afterwards, the algorithms are parted further which undergoes more processing in similar manner. Finally, the results of various partitions are fused together. The incremental algorithm pushes the data mining procedure into beginning phase.

 Variant Issues:

  1. Managing relational as well as complex data types: Many structures of data can be complicated to manage as it may be in the form of tabular, media files, spatial and temporal data. Mining all data types in one go is tougher to do.
  2. Data mining from globally present heterogeneous databases: Since databases are fetched from various data sources available on LAN and WAN. These structures can be in organized and semi-organised. Thus, making them streamlined is the hardest challenge.