We are having a lot of data but do not have knowledge. Data Mining is basically a misnomer lot of people understand data mining in the wrong way, like if you are doing gold mining or doing coal mining and, in those cases, we are digging for the gold. Whereas 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 and the reason for data mining being so important is this is a huge amount of data available and fact according to statistics is the amount of data which is produced in the last two years is more than the total amount of data produced in an earlier whole century. So, it is a huge amount of data.
This data is coming from different platforms which you are connected on like social media platforms, everyday log of social media platforms like Facebook, Instagram, Twitter, etc and your posting so many things you talk on things 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 and there is a whole lot of data there also.
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Along with that all governments are getting online. So, there a lot of online records of your education other like the bank’s transactions and so much more. There are also there is a whole variety of new data generated from news, blogs and other medias. So, now only concern is how we can utilise this data to generate some sort of knowledge which can be used for decision making. It basically discovering hidden patterns from already available data. Data can be available in different forms it can be hard copy, can be soft copy, can be online records, it can be clicks on the media, it can be keys in the browser. But if that can help us and find out some patterns, some knowledge that it what the data mining is all about. Also extracting knowledge from the data which can make some decision making effective. It is also extraction of interestingness.
Difference 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 it is not always that we are working with the same kinds of data. That is a concept of data warehousing. In order 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 type of data. An additional task is also that we need pre-processing on the issues related to the data from different sources. So that is pre-processing is performed and that is the reason it gives you more effective results.
It can be used in different parameters, different fields, in a lot of ways. So, medical field as we are mentioning here is one of the fields which is getting benefited a lot by data mining. Specially cancer detection, diabetes prediction and a lot of other things. So, data mining is one of the causes of cure for a lot of you know critical diseases and if not, the cure it is helping us reduce the effects of the diseases. E commerce is the only reason which 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 and becoming the biggest company is because of the data mining and the machine learning algorithms which they have developed. Web page analysis the results which we get prediction algorithms in the stock market and so much more.
Data we can mine is like you can mine the 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 is available through IoT. There is a huge 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 can also be mined. So, there are a lot of applications we can also apply data mining and visualising and graphs are also. So, this is what applications of data mining are and in general, it is a very broad category.
What are the major issues in data mining, because then we are talking about the diversity of the data? When we are talking about the huge amount of data along with the benefits there are a lot of advantages and disadvantages. First is the diversity itself can sometimes lead us to miss conceptualised knowledge, efficiency and scalability can be an issue and data also processing them require hardware and structure which is not always available. User interaction is a problem which we have, sometimes the data mining we must concern about this society but this kind of information we can mine or not. Data mining in general is a very wide field.