Consider a feature set containing values of operating condition of an automation unit such as pressure of the valve, temperature, fuel consumption. I want to discover unknown relationship between these variables, to come up with some functional relationship as well.
Another case can be to detect fire from sensor nodes which collects information by sensors, e.g., temperature, light, and smoke. But the signature profile is not known. In this example, it is intuitive to know when a fire occurs which is when temperature, light and smoke all three are present then there is a fire hazard. Therefore, there is a need to discoversome correlation between the attributes. Which data mining approach is suitable to discover such relation/pattern?
I want a machine learning method for feature learning so as to 1) identify any patterns that aren't obvious and 2) allow an algorithm to identify signatures of patterns in the data. For example, to see a correlation between the pressure and temperature to suggest equipment failure, say if temperature is high and pressure is low or both high or both low. These kinds of patterns and correlation need to be discovered from the data.
Question1: Can somebody please suggest some data mining approaches to solve this kind of forecast/ alert problem.
Question2: Is this a pattern recognition problem -- if so, is it supervised or unsupervised?
Question3: Can somebody please point out some examples/research articles which are related to this kind of a problem such that I can get a clear picture on how to solve it. Than you very much.