We have a lot of data but do not know. Data Mining is a misnomer lot of people understand data mining in the wrong way, like if you are doing gold mining or coal mining and, in those cases, you are digging for the gold. Here you are not mining for the data; you are mining for the knowledge. So data mining is also known as knowledge mining or knowledge discovery in data mining. Data mining is so important because of the massive amount of data available, and according to statistics, the amount of data produced in the last two years is more than the total amount of data built in an earlier whole century. So, it is a massive amount of data.
This data is coming from different platforms which you are connected on like social media platforms, daily log of social media platforms like Facebook, Instagram, Twitter, etc. and your posting so many things you talk on something like you share everything then there are e-commerce platforms which are taken a boom Amazon eBay Flipkart etc. where people go online products check out come back to the analysis of the different products. There is a whole lot of data there, also.
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Along with that, all governments are getting online. So, there are many online records of your education, other than the bank’s transactions and much more. There is also a whole variety of new data generated from news, blogs and other media. So, now the only concern is how we can utilise this data to create knowledge that it can use for decision making. It is discovering hidden patterns from already available data. Data can be available in different forms. It can be a hard copy, soft copy, or online record; it can click on the media and be keyed in the browser. But if that can help us find some patterns, some knowledge of what data mining is all about. Also, extracting knowledge from the data can make some decision-making effective. It is also an extraction of interestingness.
Differences in Mining and Searching
What is the difference, so data mining is not searching? How is it different from searching? We are not writing queries. It is not the database. Data mining is applied to various forms of data, so we are not always working with the same kinds of data. That is a concept of data warehousing to understand in simple words. Data mining is not applied to a single type of data, whereas query processing or maybe writing the query in the databases in the same data. An additional task is also that we need pre-processing on the issues related to the data from different sources. So, pre-processing is performed, which is why it gives you more effective results.
Advantages
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Users can use it in different parameters, and other fields, in a lot of ways. So, the medical field we are mentioning here is one of the fields which is getting benefited a lot from data mining. Especially cancer detection, diabetes prediction and a lot of other things. So, data mining is one of the causes of a cure for many of you know acute diseases; if not, the drug is helping us reduce the effects of the conditions. E-commerce is the only reason that data mining came into existence, and data mining is one of the reasons that E-commerce is booming; the rise of Amazon and the rise of Amazon becoming the most prominent company is because of data mining and machine learning algorithms that they have developed. Web page analysis, the results we get prediction algorithms in the stock market, and much more.
Data we can mine is like you can mine relational databases. We have data warehouses of different types for a period accounting or industry, or institute might have a lot of data. So, we can, you know, go online and get those data to perform the pre-processing and find out if there are some patterns or interestingness in there. There are sensor data available through IoT. There is a vast world of IoT getting connected with the help of which general, sports are the field which is benefiting a lot. Time series data if there is a data or the failure of time which we can use in a particular application that it can also mine. So, there are a lot of applications we can also apply data mining and visualising, and graphs are also. So, this is what data mining applications are; generally, it is an inclusive category.
Disadvantages
What are the major issues in data mining? Then we are talking about the diversity of the data. When we are talking about the massive amount of data and the benefits, there are a lot of advantages and disadvantages. First, diversity can sometimes lead us to miss conceptualised knowledge, efficiency and scalability can be an issue, and data processing requires hardware and structure, which is not always available. User interaction is a problem we have; sometimes, with data mining, we must be concerned about this society, but this kind of information can be mined or not. Data mining, in general, is a vast field.
