Intelligent decision: towards interpreting the Pe Algorithm
  The human intelligence lies in the algorithm, the nature of algorithm lies in
the classification, and the classification is equal to outlier detection. A lot
of algorithms have been proposed to detect outliers, meanwhile a lot of
definitions. Unsatisfying point is that definitions seem vague, which makes the
solution an ad hoc one. We analyzed the nature of outliers, and give two clear
definitions. We then develop an efficient RDD algorithm, which converts outlier
problem to pattern and degree problem. Furthermore, a collapse mechanism was
introduced by IIR algorithm, which can be united seamlessly with the RDD
algorithm and serve for the final decision. Both algorithms are originated from
the study on general AI. The combined edition is named as Pe algorithm, which
is the basis of the intelligent decision. Here we introduce longest k-turn
subsequence problem and corresponding solution as an example to interpret the
function of Pe algorithm in detecting curve-type outliers. We also give a
comparison between IIR algorithm and Pe algorithm, where we can get a better
understanding at both algorithms. A short discussion about intelligence is
added to demonstrate the function of the Pe algorithm. Related experimental
results indicate its robustness.
