Frequent pattern mining in EDGAR log files

Frequent pattern mining – or association rule learning – is a concept from machine learning used to discover strong rules in large databases. Popular applications are for example supermarket transactions: {tooth brush} → {tooth paste}. People who buy tooth brushes are likely to buy toothpaste as well. Another example could be {butter, bread, milk} → {cheese}.

We found ones responsible for Facebook’s Cambridge Analytica data breach – and it is you

Couple days ago, when Mark Zuckerberg, the billionaire founder and chief executive of Facebook faced senators on the House side of Capitol Hill, for two-day detailed questioning by more than 100 lawmakers, he didn’t break a sweat. It was, by basketball lingo, a serious mismatch in the paint and posterizing was inevitable. CEO of biggest social network came well prepared and next two halves of the game that lasted more than 20 hours he spent explaining to longstanding senators how social network (and internet) actually work. Mr Zuckerberg completed his job successfully, once again. He protected his empire for which he stated “has no known competitors”, and investors gave him thumbs up on the stock exchange next morning.  The biggest takeaway from the press was following: senators don’t understand how Facebook works. And they are not alone. If you have a Facebook profile, chances are, you don’t understand too.