prompt us to consider the AI in a different way. We approach the problem from an ontological way, which seems to reflect better the hierarchical structure of the mind. The פ algorithm, in such background, being presented on the studies to physics, physiology, psychology, philosophy and pattern recognition (Pe), is exploited to make intelligent decision. We analyses the outlier problem in detail in section 2, and give not only useful but also clear definitions. Based on them, an effective RDD algorithm is developed in section 3, which converts outlier problem to pattern recognition. In section 4, in order to give a better catching, we compare the פ algorithm with its subset, the IIR algorithm. The longest k–turn subsequence problem is discussed in section 5, followed by a complete solution to curve-type outlier in section 6. Section 7 presents related experiments. We offer a short discussion in section 8 and our conclusions and comments follow in section 9.

## 2. The Nature Of Outlier

 Outlier problem could be traced to its origin in the middle of the eighteenth century (Barnett and Lewis, 1994), when the main discussion was about justification to reject or retain an observation. From then, most methods have been developed by probability and statistics (Peirce, 1852; Chauvenet, 1863; Edgeworth, 1887; Grubbs, 1969; Huber, 1973; Hampel, 1974, Tukey, 1977; Rousseeuw and Yohai, 1984). As in reality, the distribution of a data set is not always certain, statistical methods have their limits (Maronna et al., 2006). Besides these methods, many other kinds of algorithms have been proposed: the distance-based (Knorr and Ng, 1999), the density-based (Breunig et al., 2000), the neural network (Connor et al., 1994; Bullen et al., 2003), the deviation-based (Arning et al, 1996; Jagadish et al., 1999) and the genetic algorithm (Baragona, 2001), etc. Meanwhile a lot of ad hoc definitions are presented. Hampel (2001) evaluated the concept of outlier "without clear boundaries" and some method like Grubbs" rule "can barely detect one distant outlier out of 10" (Hampel, 1985). Jagadish et al. (1999) also stated: each of them gives a different answer, and none of them is conceptually satisfying. Here we are making clear the definition of an outlier and present related solution. Let"s first see a well-quoted intuitive definition of outlier by Hawkins (1980).

An outlier is an observation that deviates so much from other observations as to arouse suspicions that it was generated by a different mechanism.

And then a definition from Barnett and Lewis (1994). An observation (or subset of observations) which appears to be inconsistent with the remainder of that set of data.

Barnett and Lewis (1994) stated that "the phrase "appears to be inconsistent" is crucial to 