What is the difference between Data Mining and Machine Learning?
Data Mining is extracting knowledge from huge amount of data. It is used to get rules from the existing the data. The origin of Data mining is the traditional Databases with unstructured Data. It involves more human interference. It is used in Clustering Analysis. Data mining tools search for meaning in all this information. Data mining finds patterns in apparently unrelated data and putting it together to predict future outcomes. Data mining pulls together data based on the information it mines from various data sources, it doesn’t drive any processes on its own.
Machine Learning is a technique of analyzing data, learn from that data and then apply what they have learned to a model to make a knowledgeable decision. Machine Learning is introducing new algorithm from the data as well as past experience. It teaches the computer to learn and understand given rules. Machine Learning is Automated. It is used in web search, spam filter, fraud detection.
AI applications use datasets fed to them updating them of what type of behavior to get ahead. Algorithms guides towards the correct source of information to address each request put to them. Machine learning uses datasets formed from mined data. Algorithms receive this information and use it to build instructions defining the actions taken by AI applications. Without this information, AI would not know how to respond when someone makes any type of request.