A Review of Data Security Primitives in Data Mining

This paper has discussed various issues and security primitives like Spatial Data Handing, Privacy Protection of data, Data Load Balancing, Resource Mining etc. in the area of Data Mining.A 5-stage review process has been conductedfor 30 research papers which were published in the period of year ranging from 1996 to year 2013. After an exhaustive review process, nine key issues were found “Spatial Data Handing, Data Load Balancing, Resource Mining ,Visual Data Mining, Data Clusters Mining, Privacy Preservation, Mining of gaps between business tools & patterns, Mining of hidden complex patterns.” which have been resolved and explained with proper methodologies. Several solution approaches have been discussed in the 30 papers. This paper provides an outcome of the review which is in the form of various findings, found under various key issues. The findings included algorithms and methodologies used by researchers along with their strengths and weaknesses and the scope for the future work in the area

Actionable knowledge discovery : methodologies and frameworks

University of Technology, Sydney. Faculty of Engineering and Information Technology.Most data mining algorithms and tools stop at the mining and delivery of patterns satisfying expected technical interestingness. There are often many patterns mined but business people either are not interested in them or do not know what follow-up actions to take to support their business decisions. This issue has seriously affected the widespread employment of advanced data mining techniques in greatly promoting enterprise operational quality and productivity. In this thesis, a formal and systematic view of actionable knowledge discovery (AKD for short) has been proposed from the system and microeconomy perspectives. AKD is a closed-loop optimization problem-solving process from problem definition, framework/model design to actionable pattern discovery, and to deliver operationalizable business rules that can be seamlessly associated or integrated with business processes and systems. To support AKD, corresponding methodologies, frameworks and tools have been proposed with case studies in the real world to address critical challenges facing the traditional KDD and. to cater for crucially important factors surrounding real-life AKD. First, a comprehensive survey and retrospection on the existing data mining methodologies, issues and challenges in actionable knowledge discovery are reviewed. Second, a practical data mining methodology: domain driven data mining is addressed. Third, several frameworks have been proposed to support domain drivenactionable knowledge discovery. Fourth, case studies of domain-driven actionable pattern mining in stock markets and social security data are presented to demonstrate the usefulness and potential of the proposed domain driven actionable knowledge discovery. In summary, this thesis explores in detail how domain driven actionable knowledge discovery can be effectively and efficiently applied to the discovery and delivery of knowledge satisfying both technical and business concerns as well as to support smart decision-making in the real world. The issues and techniques addressed in this thesis have potential to promote the research on critical KDD challenges, and contribute to the paradigm shift from data-centered and technical significance-oriented hidden pattern mining to domain-driven and balanced actionable knowledge discovery. The proposed methodologies and frameworks are flexible, general and effective to be expanded and applied to mining real-life complex data for actionable knowledge

Extracting actionable knowledge to increase business utility in sport services

The increase in retention of customer in gyms and health clubs is nowadays a challenge that requires concrete and personalized actions. Traditional data mining studies focused essentially on predictive analytics, neglecting the business domain. This work presents an actionable knowledge discovery system which uses the following pipeline (data collection, predictive model, loyalty actions). In the first step, it extracts and transforms existing real data from databases of the sports facilities. In a second step, predictive models are applied to identify user profiles more susceptible to dropout. Actionable rules are generated based on actionable attributes that should be avoided, in order to increase retention. Finally, in the third step, based on the previous actionable knowledge, experimental planning is carried out, with test and control groups, in order to find the best loyalty actions for customer retention. This document presents a simulation and the measure of the business utility of an actions sequence to avoid dropout.info:eu-repo/semantics/publishedVersio

Mining Fuzzy Coherent Rules from Quantitative Transactions Without Minimum Support Threshold

[[abstract]]Many fuzzy data mining approaches have been proposed for finding fuzzy association rules with the predefined minimum support from the give quantitative transactions. However, some comment problems of those approaches are that (1) a minimum support should be predefined, and it is hard to set the appropriate one, and (2) the derived rules usually expose common-sense knowledge which may not be interested in business point of view. In this paper, we thus proposed an algorithm for mining fuzzy coherent rules to overcome those problems with the properties of propositional logic. It first transforms quantitative transactions into fuzzy sets. Then, those generated fuzzy sets are collected to generate candidate fuzzy coherent rules. Finally, contingency tables are calculated and used for checking those candidate fuzzy coherent rules satisfy four criteria or not. Experiments on the foodmart dataset are also made to show the effectiveness of the proposed algorithm.[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20120610~20120615[[iscallforpapers]]Y[[conferencelocation]]Brisbane, Australi

Mining unexpected patterns using decision trees and interestingness measures: a case study of endometriosis

[[abstract]]Because clinical research is carried out in complex environments, prior domain knowledge, constraints, and expert knowledge can enhance the capabilities and performance of data mining. In this paper we propose an unexpected pattern mining model that uses decision trees to compare recovery rates of two different treatments, and to find patterns that contrast with the prior knowledge of domain users. In the proposed model we define interestingness measures to determine whether the patterns found are interesting to the domain. By applying the concept of domain-driven data mining, we repeatedly utilize decision trees and interestingness measures in a closed-loop, in-depth mining process to find unexpected and interesting patterns. We use retrospective data from transvaginal ultrasound-guided aspirations to show that the proposed model can successfully compare different treatments using a decision tree, which is a new usage of that tool. We believe that unexpected, interesting patterns may provide clinical researchers with different perspectives for future research.[[incitationindex]]SCI[[incitationindex]]EI[[booktype]]紙本[[booktype]]電子