Data mining and big data are definitely not the same in terms of definition. But how are their applications different from one another? do they even differ at all?
I thing that Big Data is a term referring to the data that have huge size, high velocity, variety and so on. However, Data Mining it's a techniques inside big data analytics paradigm that used pre-processed and refined data input to extract useful insights based in mathematical models of course helping in decision makers. So Data Mining is a part of Big Data analytics.
Conceptually both may mine data. But when you are applying mining alogorithm for huge tera byte of particularaly unstructured data then it can be categorized as big data. Data mining mostly works with structured and centralized data sources may be for a particular organization
Big Data refers to a huge volume of data that can be structured, semi-structured and unstructured. Usually, data which is equal to or greater than 1 Tb known as Big Data. The importance of Big Data does not mean how much data we have but what would you get out of that data. while, Data Mining (also known as Knowledge Discovery of Data) refers to extracting knowledge from a large amount of data i.e. Big Data. It is mainly used in statistics, machine learning and artificial intelligence. It is the step of the “Knowledge discovery in databases”.
When we refer to big data, we are not talking about the knowledge in it because it may be unstructured and may be difficult to get meaningful information from it. However, when we transform the large data, and apply statistical techniques to get useful information and for decision making process, then we refer to that as data mining. In other words, data mining is application of statistical procedures to big data to get meaningful information.
I think that we use techniques and algorithms from data mining for big data. More data give us more accuracy, and with big data technology and data mining approach we can create new knowledge, manage business efficiently, and be more creative.