The measure of information being created and stored daily is too massive. A current report evaluated that consistently, Google gets more than 2 million queries, email users send more than 200 million messages, YouTube users transfer 48 hours of video, Facebook users share more than 680,000 bits of content, and Twitter users create 100,000 tweets.
In addition, media sharing websites, stock exchanging sites and news sources consistently heap up more new information for the duration of the day.
A couple of years back, when we started to use this “Big Data” to discover reliable insights and patterns and very quickly, another interrelated research zone rose: Data Mining.
In this article, we investigate 12 big and common issues encountered in Data Mining.
1. Poor information quality, for example, boisterous data, grimy information, missing values, estimated or mistaken values, deficient data size and poor portrayal in data sampling.
2. Incorporating clashing or repetitive information from various sources and structures: mixed media records (sound, video and pictures), geo information, content, social, numeric, and so on…
3. Expansion of security and protection worries by people, associations and governments.
4. Inaccessibility of information or troublesome access to information.
5. Proficiency and adaptability of data mining algorithms to successfully separate the data from tremendous measure of information in databases.
6. Managing immense datasets that require circulated approaches.
7. Managing non-static, unequal and cost-delicate information.
8. Mining data from heterogeneous databases and worldwide data frameworks.
9. Consistent updation of models to deal with information speed or new approaching information.
10. High cost of purchasing and keeping up effective programming projects, servers and capacity equipment types that handle a lot of information.
11. Handling of huge, intricate and unstructured information into an organized configuration.
12. Sheer amount of yield from numerous information mining strategies.