Bias or not when finding patterns using data mining techniques?
In data mining, is it okay to start from expectations (bias) as to which patterns could be present and actually do statistics to see if this is actually the case


Sure. This is called hypothesis testing.


  Say if i have data for the typical example of survivors on the titanic. How would i start doing my analysis - that is: what types of questions would i be asking my self to begin with


Exploratory data analysis, see http://github.com/MartinThoma/edapy as a tool.


  how does one go from A (wanting to find patterns) to B (actually finding unknown patterns)) in data mining specifically?


You have a classification problem... So, just train the classifier you want to use.


  If i have a large dataset which i have cleaned and prepared, then from a data mining perspective what is the following thought-process I should use to find patterns in the data?


This depends very much on the context. What I happen to do at the very beginning quite often:


Look at the general distribution of the data per single feature: min, max, mean, median, number of different values. If the number of different values is small, print a bar chart
Null values: why are they there? Can they be replaced by a reasonable other value?
Covariance



  What is the difference between ML and data mining?


Taken from 
https://stats.stackexchange.com/a/29186/25741


  Data Mining is about using Statistics as well as other programming methods to find patterns hidden in the data so that you can explain some phenomenon. Data Mining builds intuition about what is really happening in some data and is still little more towards math than programming, but uses both.
  
  Machine Learning uses Data Mining techniques and other learning algorithms to build models of what is happening behind some data so that it can predict future outcomes. Math is the basis for many of the algorithms, but this is more towards programming.