Data Mining And Knowledge Discovery Research Articles

Publisher's Note: Wang, C., Zhang, Q., Liu, W., Liu, Y. & Miao, L. Facial feature discovery for ethnicity recognition. WIREs Data Mining Knowl. Discov. 9, e1278 (2019). https://doi.org/10.1002/widm.1278

WIREs Data Mining and Knowledge DiscoveryVolume 10, Issue 5 e1370 Publisher's NoteFree Access Publisher's Note: Wang, C., Zhang, Q., Liu, W., Liu, Y. & Miao, L. Facial feature discovery for ethnicity recognition. WIREs Data Mining Knowl. Discov. 9, e1278 (2019). https://doi.org/10.1002/widm.1278 This article corrects the following: Expression of Concern: Facial feature discovery for ethnicity recognition Cunrui Wang, Qingling Zhang, Wanquan Liu, Yu Liu, Lixin Miao, Volume 9Issue 1Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery First Published online: August 2, 2018 First published: 28 April 2020 https://doi.org/10.1002/widm.1370 [Correction added on 28 May 2020 after first online publication: The first and last paragraphs of this note were edited to better reflect the concerns that were raised.] AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Wiley received notification of concern about whether the ethical requirements for human subject research were met in a study by Wang, C. and colleagues that was published in WIREs Data Mining Knowledge Discovery (Wang et al., 2019) on August 2, 2018. Study participants are reported to be from ethnic or religious minority groups, with some considered part of a vulnerable population. Wiley and the WIREs editorial team launched an investigation in response to these concerns to check compliance with acceptable standards for ethics approval and consent; the investigation concluded with the decision to retain the article in the publication record. The investigation established that: All research participants were students at Dalian Minzu University and volunteered to participate. The authors provided multiple signed consent forms with signatures sufficiently distinct upon our inspection. Each consent form permits use of the participant's photo for commercial and non-commercial purposes as part of the study and use in any resulting publications. The authors verified that each participant was verbally informed of the study's aim prior to signing the consent form. The authors, during their research, gathered two data points from each participant: ethnicity and a photo, and removed all other personally identifiable information. The data used in the article was collected in 2014, at which time there was no national guidance with a cohesive set of ethical criteria related to computational or social science research. In carrying out their research, the authors first collected the data then sought university ethics approval to use the data in their research. They received approval to carry out their research on the data roughly six months after applying. The authors sought, and received, approval from their university's research ethics review committee to reuse the data each year since the initial submission in 2014. They provided the university approval forms from 2017 and 2018, which were the most recent approvals they had received prior to submitting their article to the Journal in 2018. The authors maintain that they pursued ethical approval for their research that was consistent with university, local, and national requirements at the time (2014). Based on the Journal's assessment of the evidence collected during the investigation, research participants were volunteers, were made aware of the study's intent, and provided appropriate license for data to be used in the research and, eventually, publication. Wiley and the Journal editors recognize that the authors provided definitions of ethnicity, race, and nationality within the article that do not conform to current anthropological and sociological standards. The authors’ limited understanding of these perspectives has contributed to a racialist tone throughout the article. Wiley and the Journal editors do not endorse these perspectives, but we understand that these authors are not anthropologists and that these concepts might be understood differently in different parts of the world. Our investigation concludes that the research reported by Wang and colleagues does appear to be in compliance with acceptable standards for conducting human subject research and that the authors followed the basic local and international standards for obtaining consent that could reasonably be expected at the time they conducted the research. REFERENCE Wang, C., Zhang, Q., Liu, W., Liu, Y., & Miao, L. (2019). Facial feature discovery for ethnicity recognition. WIREs Data Mining Knowl. Discov., 9, e1278. https://doi.org/10.1002/widm.1278 Volume10, Issue5September/October 2020e1370 ReferencesRelatedInformation

The use of big data and data mining in nurse practitioner clinical education.

Nurse practitioner (NP) faculty have not fully used data collected in NP clinical education for data mining. With current advances in database technology including data storage and computing power, NP faculty have an opportunity to data mine enormous amounts of clinical data documented by NP students in electronic clinical management systems. The purpose of this project was to examine the use of big data and data mining from NP clinical education and to establish a foundation for competency-based education. Using a data mining knowledge discovery process, faculty are able to gain increased understanding of clinical practicum experiences to inform competency-based NP education and the use of entrusted professional activities for the future.

CRISP-eSNeP: Towards a data-driven knowledge discovery process for electronic social networks

ABSTRACTValue in big data is created when insights are mined to support business processes. On social networks (SNs), big data, coupled with the operational mechanisms of such networks presents challenging, yet interesting perspectives to generate insights. A key limitation to big data research on SNs is the lack of a concise methodological model that drives conceptual and analytical questions. We add specificity to existing Knowledge Discovery and Data Mining (KDDM) frameworks by proposing a methodology for analyzing big data on electronic SNs. Particularly, we propose the Cross Industry Standard Process for Electronic Social Network Platforms (CRISP-eSNeP) not only as an extension to the CRISP-DM model, but also as an advancement of the knowledge on KDDM methods. Our method emphasizes the efficient management of large semi-structured and unstructured data that reflects a specific SN. We present the results of our process development using Gregor and Hevner’s design science research schema.

A tourist walk approach for internal and external outlier detection

Outlier detection is a fundamental task for knowledge discovery in data mining, especially in the Big Data era. It aims to detect data items that deviate from the general pattern of a given data set. In this paper, we present a new outlier detection technique using tourist walks starting from each data sample and varying the memory size. Specifically, a data sample gets a higher outlier score if it participates in few tourist walk attractors, while it gets a low score if it participates in a large number of attractors. Experimental results on artificial and real data sets show good performance of the proposed method. In comparison to classical outlier detection methods, the proposed one shows the following salient features: (1) It finds out outliers by identifying the structure of the input data set instead of considering only physical features, such as distance, similarity or density. (2) It can detect not only external outliers as classical methods do, but also internal outliers staying among various normal data groups. (3) By varying the memory size, the tourist walks can characterize both local and global structures of the data set. (4) A parallel implementation is quite convenient due to the nature of large amount of independent walking of the algorithm. (5) The proposed method is a deterministic technique. Therefore, only one run is sufficient, in contrast to stochastic techniques, which require many runs. Moreover, in this work, we find, for the first time, that tourist walks can generate complex attractors in various crossing shapes. Such complex attractors reveal data structures in more details. Consequently, it can improve the outlier detection performance.

Towards an integrated process model for new product development with data-driven features (NPD3)

New information and communication technologies are changing the way products are developed, manufactured, serviced and managed over the product’s lifecycle. Today’s smart products not only consist of their physical components, but are also endowed with intelligence. Data and the capabilities to process data into knowledge and eventually decisions have become critical components of the product itself and of the process to develop/operate the product. This paper investigates how engineers and a new functional role, data scientists, can effectively collaborate in a mixed team for new product development with data-driven features (NPD3). We focus on the concept development stage, typically the fuzziest phase of product development. In this paper, an integrated process model is explored by revisiting the traditional new product development (NPD) process model as well as the knowledge discovery and data mining (KDDM) process model. Then a case study of the development of an application-specific unmanned aircraft system (UAS) is used to examine the proposed model.

Qualitative usability feature selection with ranking: a novel approach for ranking the identified usability problematic attributes for academic websites using data-mining techniques

ObjectiveThe aim of this study is to identify common usability problematic patterns that belong to top-50 academic websites as a whole and then ranking of these identified usability problems is also provided.MethodsIn this study, a novel approach is proposed that is based upon the integration of conventional usability testing and heuristic evaluation with data-mining knowledge discovery process. An experiment is conducted to evaluate ISO 9241-151 guidelines under 16-different categories by hundred participants who are frequent users of academic websites. After evaluation, the qualitative usability data is collected and different data-mining techniques i.e. association rule and decision tree are applied to recognize fully functional and problematic usability attributes. Identified problematic attributes represent common usability problems patterns related to academic websites from the qualitative viewpoint only. This study further prioritizes these problematic attributes by using the ranking algorithm that represents the order in which usability issues must be resolved.ResultsIn this study, 16-different categories are considered for usability evaluation of academic websites. The results show that no issues are identified in two-categories i.e. {Headings_Titles_Labels and The Home_Page}. In Scrolling and Paging category, horizontal scrolling is identified as a major issue whereas, in Internationalization category, the users do not identify supported languages on most of the academic websites. Users do not find websites to be highly secured under Security category. Our findings investigate that most of the issues are found in Search and Social Media categories. Furthermore, users easily locate 50.53% guidelines on websites as fully functional whereas, 49.46% of characteristics are considered as problematic usability features that are not functional on the academic website as a whole.ConclusionsIdentification of common usability problems at an early stage can lower substantially the development efforts in cost and time. Software developers can restrain from these potential usability problems during the development of novel systems under the same context. Providing appropriate solutions for these problems can become valuable in software development. The proposed approach concludes that conventional usability evaluation methods can go beyond just than testing of systems. The study is a milestone towards identification and prioritizing problematic usability features for academic websites and helps in providing the wholistic approach of usability problematic patterns for web-domain.

ENHANCING BUSINESS PROCESS THROUGH RECOMMENDATION SYSTEM BASED ON RANKING AND QUERY REFORMULATION

In Business process data mining and knowledge discovery plays a vital role which is more difficult to process due to the availability and enhancing nature of data every day. There is a gap between what the user wants and what the system perceives as needed by the user. One of the ways to enrich business is to personalize the websites for each user by understanding their need, interest and behavior. The main challenge is information overloading and user dynamic nature. Even for a sample of small content availability in the domain, the mismatch is significant. Information retrieval research has shown over the years that the focus of the users is over a short and limited number of results. Getting the accuracy of the results is thus a significant need. Recommendation systems for Business have focused on the match between the user and the content through methods that focus on personal profiles, server ranking and user query processing. This is the focus of this paper where authors propose. This model uses the advances in information retrieval research and leverages the basic pedagogical models. The experimental results have shown promise and thrown up some interesting challenges for the future.

The 2010 KDD Cup Competition Dataset: Engaging the machine learning community in predictive learning analytics

In the spring of 2010, the Association for Computing Machinery (ACM) Special Interest Group on Knowledge Discovery and Data-mining (KDD) selected a dataset from an educational technology for its annual competition. The competition, titled “Educational Data Mining Challenge”, tasked participants with predicting the correctness of student answers to questions within an Intelligent Tutoring System (ITS) from The Cognitive Tutors suite of tutors. This challenge was hosted by the PSLC DataShop, and included data provided by the Carnegie Learning Inc., producers of The Cognitive Tutors. Consisting of over 9GB of student data this was the largest KDD Cup dataset up to that point in time. The competition brought in 655 competitors submitting 3,400 solutions. Five years later, we believe the competition dataset has been the most often cited from an educational technology platform.

Ontology-based data mining model management for self-service knowledge discovery

Data mining (DM) models are knowledge-intensive information products that enable knowledge creation and discovery. As large volume of data is generated with high velocity from a variety of sources, there is a pressing need to place DM model selection and self-service knowledge discovery in the hands of the business users. However, existing knowledge discovery and data mining (KDDM) approaches do not sufficiently address key elements of data mining model management (DMMM) such as model sharing, selection and reuse. Furthermore, they are mainly from a knowledge engineer’s perspective, while the business requirements from business users are often lost. To bridge these semantic gaps, we propose an ontology-based DMMM approach for self-service model selection and knowledge discovery. We develop a DM3 ontology to translate the business requirements into model selection criteria and measurements, provide a detailed deployment architecture for its integration within an organization’s KDDM application, and use the example of a student loan company to demonstrate the utility of the DM3.

An effort to select a preferable metaheuristic model for knowledge discovery in data mining

Swarm intelligence computation is becoming an important topic in the field of decision making, and is successfully applied in many fields, which is indicating a fairly great potential for development. Recently, much research work has been carried out by swarm intelligence in different fields viz. sensor network, robotic management, microwave filter design, data mining applications etc. In this paper, an effort has been made to make a comparison on the performance of fuzzy logic with different membership function, artificial neural network and swarm intelligence models. The models with minimum errors have been given preference for selection towards the decision making of information. The same methods can also be cross-checked by residual analysis and a new unknown data set to verify the earlier proposed observation. Initially the statistical techniques have been applied on the data set to select the preferable models but it has been observed that statistical technique errors are large. Therefore, under the fuzzy logic, different membership functions have been used to select the preferable membership function for fuzzy logic for the available data. In this paper further effort has also been made to change the expression of the computing fitness function of the path in artificial bee colony to achieve the best result.

Knowledge Discovery in Medical Data by using Rough Set Rule Induction Algorithms

In the performance of data mining and knowledge discovery activities, rough set theory has been regarded as a powerful, feasible and effective methodology. There is a need for analysis of medical data that deals with incomplete and inconsistent information with the tremendous manipulation at different levels. In this context, rough set rule induction algorithms are capable of generating decision rules which can potentially provide new medical insight and profound medical knowledge. By taking into consideration all the above aspects, the present investigation is carried out. The results clearly show that rough set approach is certainly a useful tool for medical applications. Relationships and patterns within this data could provide new medical knowledge. The genetic algorithms offer an attractive approach for solving the feature subset selection problem. The process of finding useful patterns or meaning in raw data has been called knowledge discovery in databases. The algorithms used for the present study are: Exhaustive search, Covering, LEM2 and Genetic algorithms. Rules are generated and improved in the case of the above mentioned four algorithms. Further, the generated rules are improved by applying the shortening ratio as 0.8. Some of the important results of the present investigation include maximum coverage of 100% is observed in the case of exhaustive and genetic algorithms.

An Efficient and Dynamic Data Placement, Data Mining and Knowledge Discovery Using Surrogate Object in Mobile Cloud Paradigm

Advancement in wireless and mobile communication with ubiquity of devices in cloud has the potential in storing incredible volumes of knowledge provided by data in the mobile devices. Knowledge provided by high volume of data stored in virtually infinite computing infrastructure, namely, the mobile cloud has become very essential in many day to day applications. However, mobile cloud faces many challenges such as dependency on continuous network connections, data placement problems, limitation and issues in data mining and knowledge discovery with multiple service providers. In this paper a novel data placement and knowledge discovery technique, SD2-Kd (Surrogate object based Dynamic Data placement and Knowledge discovery) is proposed, which interacts two or more service providers for the purpose of load balancing and data placement in cloud replication for fast and easy access. The proposed model provides a real-time data placement from different service providers using surrogate object in the mobile support station and an exact copy updated with the database and mining server in synchronized and unsynchronized manner. The model also allows mobile devices to participate seamlessly in mining process and act on behalf of mobile device and permit to cache the location based frequent datasets and retrieve the knowledge from those datasets which in turn provides better response time and minimizes the overall network traffic incurred due to mobility and database server failover. It handles the data placement and mining process at the object level. An extensive simulation of the D2S-Kd technique has been done and it shows that the proposed technique improves the response times, achieves better bandwidth utilization, provides support for disconnection and increases the success rate of mining progressively more than the existing techniques.

Methodology and Framework of a Cloud-Based Prognostics and Health Management System for Manufacturing Industry

Prognostics and Health Management (PHM) as a research discipline has been extensively studied in recent years, with many sophisticated techniques and intelligent algorithms developed for machinery data analysis, health assessment and decision making. PHM solutions for typical components such as roller bearing and machine tool have grown and proved to be reliable enough for industrial application. However utilization of PHM solutions in industry is still much limited due to high research, development and implementation costs. Such limitations are more severe in smaller scale factories and workshops where noample resource is available for implementing PHM systems. Efforts have been made to tackle such issue, for instance, Watchdog Agent® Toolbox developed by the IMS Center enables faster deployment of PHM solutions through algorithm modularization and standardization. With advantages that can be deliveredwith the emerging cloud computing paradigm, a cloud-based prognostics and health management system for manufacturing industry has been developed based on Watchdog Agent® tools and the ideology of PHM as a Service. In addition to traditional data acquisition and management functions in a machine conditionmonitoring system, the cloud based PHM platform is able to further provide on-demand, customizable and low-cost data analysis service. Machinery data accumulated within the cloud system further enables more advanced services such as machine-to-machine comparison, data mining and knowledge discovery.

Using Multi-Vector DBSCAN Algorithm to Cluster the Distribution of Urban Water Point

Spatial clustering is an important method for spatial data mining and knowledge discovery. According to the deficiency existing in density-based clustering algorithm DBSCAN ,such as the I/O overhead, memory consumption etc. This paper improves the DBSCAN algorithm ,which proposed directional density algorithm, the algorithm reduces lots of points which need to be queried. By taking Geographic Information System for the application background, we successfully applied to forecast the distribution of urban water points. Compared with the traditional DBSCAN algorithm, the results conformed to the actual situation, and efficiency increased by 20%.

Profiling internet banking users: A knowledge discovery in data mining process model based approach

Analysing datasets using data mining techniques can enhance decision making in organizations. However, to ensure that the full potential of these techniques is realised it is important that decision makers understand there are Knowledge Discovery and Data Mining (KDDM) processes that are mature enough to be adopted. This paper demonstrates the benefits of using a KDDM process to evaluate survey data for internet banking users in Jamaica which includes demographic as well as attitudinal and behavioral variables. The major benefits of following this process include the selection of a set of models, rather than a single model, which are more relevant to the business/research objectives and use of a more targeted knowledge discovery process as the data mining analyst is now directed to consider the effects the decisions in each phase will have on subsequent phases. This leads to more relevant knowledge being extracted from the data mining process.

A social intelligent system for multi-objective optimization of classification rules using cultural algorithms

Cultural algorithms (CA) use social intelligence to solve problems in optimization. The CA is a class of evolutionary computational models inspired from observing the cultural evolutionary process in nature. Cultural algorithms employ a basic set of knowledge sources, each related to knowledge observed in various animal species. Knowledge from these sources is then combined to influence the decisions of the individual agents in solving problems. Classification using "IF-THEN" rules comes under descriptive knowledge discovery in data mining and is the most sought out by users since they represent highly comprehensible form of knowledge. The rules have certain properties which make them useful forms of actionable knowledge to the users. The rules are evaluated using these properties represented as objective and subjective measures. The rule properties may be conflicting. Hence discovery of rules with specific properties is considered as a multi-objective optimization problem. In the current study an extended cultural algorithm which applies social intelligence in the data mining domain to present users with a set of rules optimized according to user specified metrics is proposed. Preliminary experimental results using benchmark data sets reveal that the algorithm is promising in producing rules with specific properties.

Generation of explicit rules predicting neuroblastoma patients’ outcome

Generation of explicit rules predicting neuroblastoma patients’ outcome

Cultural Algorithm Toolkit for Multi-objective Rule Mining

Cultural algorithm is a kind of evolutionary algorithm inspired from societal evolution and is composed of a belief space, a population space and a protocol that enables exchange of knowledge between these sources. Knowledge created in the population space is accepted into the belief space while this collective knowledge from these sources is combined to influence the decisions of the individual agents in solving problems. Classification rules comes under descriptive knowledge discovery in data mining and are the most sought out by users since they represent highly comprehensible form of knowledge. The rules have certain properties which make them useful forms of actionable knowledge to users. The rules are evaluated using these properties namely the rule metrics. In the current study a Cultural Algorithm Toolkit for Classification Rule Mining (CAT-CRM) is proposed which allows the user to control three different set of parameters namely the evolutionary parameters, the rule parameters as well as agent parameters and hence can be used for experimenting with an evolutionary system, a rule mining system or an agent based social system. Results of experiments conducted to observe the effect of different number and type of metrics on the performance of the algorithm on bench mark data sets is reported.

Evaluation of an integrated Knowledge Discovery and Data Mining process model

Data Mining projects are implemented by following the knowledge discovery process. This process is highly complex and iterative in nature and comprises of several phases, starting off with business understanding, and followed by data understanding, data preparation, modeling, evaluation and deployment or implementation. Each phase comprises of several tasks. Knowledge Discovery and Data Mining (KDDM) process models are meant to provide prescriptive guidance towards the execution of the end-to-end knowledge discovery process, i.e. such models prescribe how exactly each one of the tasks in a Data Mining project can be implemented. Given this role, the quality of the process model used, affects the effectiveness and efficiency with which the knowledge discovery process can be implemented and therefore the outcome of the overall Data Mining project. This paper presents the results of the rigorous evaluation of the Integrated Knowledge Discovery and Data Mining (IKDDM) process model and compares it to the CRISP-DM process model. Results of statistical tests confirm that the IKDDM leads to more effective and efficient implementation of the knowledge discovery process.

Algorithmic acquisition of diagnostic patterns in district heating billing system

An application of algorithmic exploration of billing data is examined for fault detection, diagnosis (FDD) based on evaluation of present state and detection of unexpected changes in energy efficiency of buildings. Large data sets from district heating (DH) billing systems are used for construction of feature space, diagnostic rules and classification of the buildings according to their energy efficiency properties. The algorithmic approach automates discovering knowledge about common, thus accepted changes in buildings’ properties, in equipment and in habitants’ behavior reflecting progress in technology and life style. In this article implementation of Data Mining and Knowledge Discovery (DMKD) method in supervision system with exemplary results based on real data is presented. Crucial steps of data processing influencing diagnostic results are described in details.