Artificial neural networks evolve into deep learning recently and perform well in various fields, such as image speech recognition translation. However, there is a problem that it difficult for person to understand what exactly the trained knowledge of an artificial network. As one methods solving network, rule extraction had been devised. In this study, rules are extracted from using ordered-a...

<div>Association rule mining is a well-known data technique used for extracting hidden correlations between items in large databases. In the majority of situations, results contain sensitive information about individuals and publishing such will violate individual secrecy. The challenge association to preserve confidentiality rules when releasing database external parties. hiding conceals...

PURPOSE To probe medical students' narrative essays as a rich source of data on the hidden curriculum, a powerful influence shaping the values, roles, and identity of medical trainees. METHOD In 2008, the authors used grounded theory to conduct a thematic analysis of third-year Harvard Medical School students' reflection papers on the hidden curriculum. RESULTS Four overarching concepts wer...

Navigating through multidimensional data cubes is a nontrivial task. Although On-Line Analytical Processing (OLAP) provides the capability to view multidimensional data through rollup, drill-down, and slicing-dicing, it offers minimal guidance to end users in the actual knowledge discovery process. In this article, we address this knowledge discovery problem by identifying novel and useful patt...

This paper proposes an inductive data mining technique (named GPR) based on genetic programming. Unlike other mining systems, the particularity of our technique is its ability to discover business rules that satisfy multiple (and possibly conflicting) decision or search criteria simultaneously. We present a step-by-step method to implement GPR, and introduce a prototype that generates productio...

In this paper, we propose a method to analyze the firewall policy or rule-set using Relational Algebra and Raining 2D-Box Model. It can discover all the anomalies in the firewall rule-set in the format that is usually used by many firewall products such as Cisco Access Control List, IPTABLES, IPCHAINS and Check Point Firewall-1. While the existing analyzing methods consider the anomalies betwee...

While larger and larger pools of stock market data are available for investors, it is crucial for them to achieve the knowledge hidden behind and make the correct selections. The huge data amount, the variable data characteristic, and the noisy environment make this goal a great challenge. Using the model of fuzzy decision tree based rules extraction, a new set of fuzzy rules to select stocks w...

This paper presents CSar, a Michigan-style learning classifier system designed to extract quantitative association rules from streams of unlabeled examples. The main novelty of CSar with respect to the existing association rule miners is that it evolves the knowledge online and it is thus prepared to adapt its knowledge to changes in the variable associations hidden in the stream of unlabeled d...