Judging Credible and Unethical Statistical Data Explanations via Phrase Similarity Graph

We propose a graph-based method to judge credible and unethical statistical data explanations with the exploitation of human instincts proposed by Rosling et al. Our previous work proposes three categories of statistical data explanations and three corresponding judgment methods based on phrase embedding and carefully designed comparison conditions. However, we observe that the previous method β exhibits low accuracy in the explanations of (β) category due to its counter-intuitive semantic similarities between several phrases. To address this limitation and improve the performance, our new method β^2 constructs a Phrase Similarity Graph to generate additional comparison conditions and devises a credibility score to aggregate the conditions with their importance quantified by graph entropy. The experimental results show that our β^2 achieves over 81% accuracy while the previous method β achieves about 57%. Scrutiny reveals that our β^2 mitigates the problem of the counter-intuitive semantic similarities at a satisfactory level

Data Mining Methods for Discovering Interesting Exceptions from an Unsupervised Table

In this paper, we survey efforts devoted to discovering interesting exceptions from data in data mining. An exception differs from the rest of data and thus is interesting and can be a clue for further discoveries. We classify methods into exception instance discovery, exception rule discovery, and exception structured-rules discovery and give a condensed and comprehensive introduction

Data Mining Methods for Discovering Interesting Exceptions

Abstract: In this paper, we survey efforts devoted to discovering interesting exceptions from data in data mining. An exception differs from the rest of data and thus is interesting and can be a clue for further discoveries. We classify methods into exception instance discovery, exception rule discovery, and exception structured-rules discovery and give a condensed and comprehensive introduction

Mining Financial Data with Scheduled Discovery of Exception Rules

Abstract. This paper shows preliminary results, on financial data, of an algorithm for discovering pairs of an exception rule and a common sense rule under a prespecified schedule. An exception rule, which represents a regularity of exceptions to a common sense rule, often exhibits interestingness. Discovery of pairs of an exception rule and a common sense rule under threshold scheduling has been successful in efficient discovery of interesting rules. In this paper, we apply it to financial data, which has been provided as a benchmark data set for data mining methods. Examples of discovered knowledge as well as a simple description of the approach are both provided in this paper

Discovering interesting exception rules with rule pair

Abstract. In this paper, we summarize a part of our 10-year endeavor for exception rule discovery. Our results mainly concern interestingness measure, reliability evaluation, practical application, parameter reduction, and knowledge representation.