Robust and cost-effective approach for discovering action rules

The main goal of Knowledge Discovery in Databases is to find interesting and usable patterns, meaningful in their domain. Actionable Knowledge Discovery came to existence as a direct respond to the need of finding more usable patterns called actionable patterns. Traditional data mining and algorithms are often confined to deliver frequent patterns and come short for suggesting how to make these patterns actionable. In this scenario the users are expected to act. However, the users are not advised about what to do with delivered patterns in order to make them usable. In this paper, we present an automated approach to focus on not only creating rules but also making the discovered rules actionable. Up to now few works have been reported in this field which lacking incomprehensibility to the user, overlooking the cost and not providing rule generality. Here we attempt to present a method to resolving these issues. In this paper CEARDM method is proposed to discover cost-effective action rules from data. These rules offer some cost-effective changes to transferring low profitable instances to higher profitable ones. We also propose an idea for improving in CEARDM method

A Bayesian network approach for causal action rule mining

Actionable Knowledge Discovery has attracted much interest lately. It is almost a new paradigm shift toward mining more usable and more applicable knowledge in each specific domain. Action Rule is a new tool in this research area that suggests some actions to user to gain a profit in his/her domain. Up to now some methods have been devised for action rule mining. Decision Trees, Classification Rules and Association Rules are three learner machines that already have been used for action rule mining. But when we want to suggest an action we need to know the causal relationships among parameters and current methods can’t say anything about that. So that we use here Bayesian Networks as one of the most powerful knowledge representing models that can show the causal relationships between variables of interest for extracting action rules. Another benefit of new method is about the background knowledge. Bayesian Networks are very powerful at integrating the background knowledge into model. At the end of this paper an action rule mining system is proposed that can suggest the most profitable action rules for each case or class of cases

Mining time series data : case of predicting consumption patterns in steel industry

Analyzing and predicting with Time series is a method which used in different fields, including consumption pattern analyzing and predicting. In this paper, required amount of inventory items have been predicted with time series. At first, desired data mining process is designed and implemented using Clementine data mining tool. We evaluate this process using the dataset from Iran's ZoabAhan steel company. Results show that by using this process not only we can model consumption patterns for the present time but also we can predict required stock items for future with adequate accuracy

Safety and efficacy of Berberis integerrima root extract in patients with type 2 diabetes. A parallel intervention based triple blind clinical trial

Purpose: To evaluate the safety and efficacy of methanol extract of Berberis integerrima root on type 2 diabetes compared to metformin. Methods: In a parallel triple blind clinical trial, 80 type 2 diabetic patients, were randomized into two groups (treated with Berberis integerrima root, 480 mg (oral), compared to control group treated with metformin 1000 mg daily). Efficacy was evaluated by fasting and prandial glucose and HbA1c and side effects confirmed by physical examination, biology and hematology tests and urinalysis on days 15, 45 and 90. They were followed for 3 months. Results: Two hundred and eighteen patients were recruited and 80 (55female and 25 male) patients randomized in two groups and 60 patient were analysed. The mean age of patients was 51.8 ± 9.3 and 46.5 ± 10 in the experimental (Berberis integerrima) and control (metformin) groups respectively (P = 0.02). The mean HbA1c at baseline was 8.1 ± 1.6 and 7.9 ± 1.6 for B. integerrima and metformin group respectively (P = 0.53), and there was no significant difference between the two groups (7.5 vs. 7.2) after 3 months (P = 0.34). Weight loss was observed in both groups compared to baseline. No adverse event led to preventing the study was reported. Conclusion: Berberis integerrima root not only was effective as much as metformin in reducing blood glucose and controlling type 2 diabetes but also, no specific side effect was reported (in short term).So, it might be an effective and safe complementary therapy in diabetic patients. Iranian Research and Clinical Trial (IRCT) registeration number; 201,207,191,774 N5. Funding: Vice chancellor for research, Physiology Research Center of Kerman University of Medical Sciences and the Exir pharmaceutical company. © 2020, Springer Nature Switzerland AG