process mining introduction 3 – decision tree

a good article hereEntropy: the degree of uncertainty invest of compressibility (zippability)Goal: reduce entropy in leaves of the tree to improve predictability. E = – (Sigma from i=1 to k)Pi log(Pi) in base 2K: possible values enumeratedPi = Ci / n is the fraction of elements having value i with Ci>= 1 the number of… Continue reading process mining introduction 3 – decision tree

Process mining – Introduction 2

Case ID Activity Name Time Stamp Play out: A possible scenario Play in: simple process allowing for 4 traces Replay Process mining: Discovery Conformance Enhancement Machine learning: Supervised learning: response variable that labels each instance (we labeled each data and the machine will learn from that) Classification: classify to predict (i.e. decision tree) Regression: final… Continue reading Process mining – Introduction 2

Process mining – Introduction 1

Process mining is the combination of Data mining and Business process management. It works with log files. Every log file must have: Case ID (order ID) Activity (purchased, Request, rejected, …) Time stamp Process mining Internet of events Big data Internet of contents (google, Wikipedia) Social media Internet of people Cloud Internet of things Mobility Internet… Continue reading Process mining – Introduction 1