Abstract. Several rule discovery algorithms have the disadvantage to discover too much patterns sometimes obvious, useless or not very interesting to the user. In this paper we propose a new approach for patterns ranking according to their unexpectedness using semantic distance calculated based on a prior background knowledge represented by domain ontology organized as DAG (Directed Acyclic Graph) hierarchy.

Sequential pattern mining is the method that has received much attention in sequence data mining research and applications, however, a drawback is that it does not profit from prior knowledge of domains. In our previous work, we proposed a belief-driven method with fuzzy set theory for discovering the unexpected sequences that contradict existing knowledge of data, including occurrence constraints and semantic contradictions. In this paper, we present a new approach that discovers unexpected sequences with determining semantic contradictions by using concept hierarchies associated with the data. We evaluate the effectiveness of our approach with experiments on Web usage analysis.

Sentiment classification in text documents is an active data mining research topic in opinion retrieval and analysis. Different from previous studies concentrating on the development of effective classifiers, in this paper, we focus on the extraction and validation of unexpected sentences issued in sentiment classification. In this paper, we propose a general framework for determining unexpected sentences. The relevance of the extracted unexpected sentences is assessed in the context of text classification. In the experiments, we present the extraction of unexpected 1 sentences for sentiment classification within the proposed framework, and then evaluate the influence of unexpected sentences on the quality of classification tasks. The experimental results show the effectiveness and usefulness of our proposed approach.

Abstract: The discovery of unexpected behaviors in databases is an interesting problem for many real-world applications. In previous studies, unexpected behaviors are primarily addressed within the context of patterns, association rules, or sequences. In this paper, we study the unexpectedness with respect to the fuzzy recurrence behaviors contained in sequence databases. We first propose the notion of fuzzy recurrence rule, and then present the problem of mining unexpected sequences that contradict prior fuzzy recurrence rules. We also develop, UFR, an algorithm for discovering the sequences containing unexpected recurrence behaviors. The proposed approach is evaluated with Web access log data.