Undirected Discovery of Interesting Exception Rules

Abstract

This paper presents an efficient algorithm for discovering exception rules from a data set without domain-specific information. An exception rule, which is defined as a deviational pattern to a strong rule, exhibits unexpectedness and is sometimes extremely useful. Previous discovery approaches for this type of knowledge can be classified into a directed approach, which obtains exception rules each of which deviates from a set of user-prespecified strong rules, and an undirected approach, which typically discovers a set of rule pairs each of which represents a pair of an exception rule and its corresponding strong rule. It has been pointed out that unexpectedness is often related to interestingness. In this sense, an undirected approach is promising since its discovery outcome is free from human prejudice and thus tends to be highly unexpected. However, this approach is prohibitive due to extra search for strong rules as well as unreliable patterns in the output. In order to circumvent these difficulties we propose a method based on sound pruning and probabilistic estimation. The sound pruning reduces search time to a reasonable amount, and enables exhaustive search for rule pairs. The normal approximations of the multinomial distributions are employed as the method for evaluating reliability of a rule pair. Our method has been validated using two medical data sets under supervision of a physician and two benchmark data sets in the machine learning community.

Mining Peculiar Compositions of Frequent Substrings from Sparse Text Data Using Background Texts

“…However, indexing scores are not for compositions of words but for single words, and the target set for TF/IDF is a single document not a set of documents like in our setting. The concept of the peculiar composition borrows its essential idea from the simultaneous discovery of exceptional rules [15,16,17,18]. It tries to discover a set of rule pairs each of which corresponds to Y → x = v and YZ → x = v , where each of Y and Z represents a conjunction of "attribute = value"s, x is an attribute, v and v are different values, and YZ is the conjunction of Y and Z.…”

“…It tries to discover a set of rule pairs each of which corresponds to Y → x = v and YZ → x = v , where each of Y and Z represents a conjunction of "attribute = value"s, x is an attribute, v and v are different values, and YZ is the conjunction of Y and Z. [15,16,18] assume that Z → x = v does not hold and in this case a rule pair corresponds to a situation that an ensemble of two conditions result in an atypical result as shown in Fig. 1.…”

Finding minimal rare itemsets with an extended version of the Apriori algorithm

“…The algorithm stops 1 The frequent zone contains the set of frequent itemsets. 2 That is, first it identifies all frequent items (attributes).…”

“…itemsets that do not occur frequently in the data (contrasting frequent itemsets). These correspond to unexpected phenomena, possibly contradicting beliefs in the domain [1,2]. In this way, rare itemsets are related to "exceptions" and thus may convey information of high interest for experts in domains such as biology or medicine.…”

Towards Rare Itemset Mining

“…itemsets that do not occur frequently in the data (contrasting frequent itemsets). These correspond to unexpected phenomena, possibly contradicting beliefs in the domain [12,16]. In this way, rare itemsets are related to "exceptions" and thus may convey information of high interest for experts in domains such as biology or medicine.…”