# Mine webshop history for clusters

I've no experience in data science so this will be one of those questions...

I have data from >100k purchases made via a webshop regarding a catalogue of around >100 items. The history of purchases flattened out looks like

Item1 Item2 ... ItemN Sex State
5     0         0   M    NY
25    15         0   F    IL
0     1         1   ?    NY


By playing around with the data, I can deduce simple facts like "90% of all purchases include at least 3 Item1", "If there are at least 4 of Item2, it is likely that Item3 is 0" or "60% of all customers from NY are male, but only 40% of those from IL are". Given the amount of combinations and data there is, the most obvious question: How can I approach wringing out more information from a data set like the above? I'm mostly interested in how one item does or does not entails inclusion of another...