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共 178 条记录，以下是91-100 订阅

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Association Rule Extraction from Medical Transcripts of Diabetic Patients

Association Rule Extraction from Medical Transcripts of Diab...

引用

5th international conference on the applications of Digital Information and Web Technologies (ICADIWT)

作者： Lakshmi, K. S. Kumar, G. Santhosh Rajagiri Sch Engn & Technol Dept Informat Technol Kochi Kerala India Cochin Univ Sci & Technol Dept Comp Sci Kochi Kerala India

ISBN: (纸本)9781479922581

Medical databases serve as rich knowledge sources for effective medical diagnosis. Recent advances in medical technology and extensive usage of electronic medical record systems, helps in massive production of medical text data in hospitals and other health institutions. Most of this text data that contain valuable information are just filed and not utilized to the full extent. Proper usage of medical information can bring about tremendous changes in medical field. This paper present a new method of uncovering valid association rules from medical transcripts. The extracted rules describes association of disease with other diseases, symptoms of a particular disease, medications used for treating diseases, the most prominent age group of patients for developing a particular disease. NLP (Natural Language Processing) tools were combined with data mining algorithms (Apriori algorithm and FP-Growth algorithm) for the extraction of rules. Interesting rules were selected using the correlation measure, lift.

关键词： Association rules data Mining Diabetes UMLS

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advances in databases and Information Systems 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Johann Eder Hele-Mai Haav Ahto Kalja Jaan Penjam

ISBN: (数字)9783540318958

ISBN: (纸本)9783540285854

The 9th East-European conference on advances in databases and Information Systems was held on September 12–15, 2005, in Tallinn, Estonia. It was organized in a cooperation between the Institute of Cybernetics at Tallinn University of Technology, the Department of Computer Engineering of Tallinn University of Technology, and the Moscow chapter of ACM SIGMOD. The main objective of the ADBIS series of conferences is to provide a - rum for the disseminationof excellent researchaccomplishmentsand to promote interaction and collaboration between the database and Information Systems research communities from Central and East European countries and the rest of the world. The ADBIS conferences provide an international platform for the presentationofresearchondatabasetheory,thedevelopmentofadvancedDBMS technologies, and their advanced applications in particular in information s- tems. The 2005 conference continued the ADBIS conferences held in St. Pete- burg (1997), Poznan (1998), Maribor (1999), Prague (2000), Vilnius (2001), Bratislava (2002), Dresden (2003), and Budapest (2004). The conference c- sisted of regular sessions with technical contributions reviewed and selected by an international Program Committee, as well as of invited talks and tutorials given by leading scientists. For the ?rst time the ADBIS conferences had a satellite event, a workshop on data mining and knowledge discovery. The ADMKD 2005 workshop,with its own international ProgramCommittee as well as proceedings, servedas a forum toencourageresearchersandpractitionerstodiscussandinvestigatedatamining research and implementation issues, and to share experience in developing and deploying data mining systems.

关键词： data Structures and Information Theory database Management Information Storage and Retrieval Information Systems applications (incl. Internet) Multimedia Information Systems User Interfaces and Human Computer Interaction

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A Modified Firefly Algorithm with Support Vector Machine for Medical data Classification 6th

A Modified Firefly Algorithm with Support Vector Machine for...

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6th IFIP international conference on Computational Intelligence and Its applications (CIIA)

作者： Sahmadi, Brahim Boughaci, Dalila Rahmani, Rekia Sissani, Noura Univ Yahia Fares Medea LMP2M Lab Medea Algeria Univ Sci & Technol Houari Boumediene Dept Comp Sci LRIA Algiers Algeria

ISBN: (纸本)9783319897431;9783319897424

Clinical information systems store a large amount of data in medical databases. In the use of medical dataset for diagnosis, the patient's information is selectively collected and interpreted based on previous knowledge for detecting the existence of disorders. Feature selection is important and necessary data pre-processing step in medical data classification process. In this work, we propose a wrapper method for feature subset selection based on a binary version of the Firefly Algorithm combined with the SVM classifier, which tries to reduce the initial size of medical data and to select a set of relevant features for enhance the classification accuracy of SVM. The proposed method is evaluated on some medical dataset and compared with some well-known classifiers. The computational experiments show that the proposed method with optimized SVM parameters provides competitive results and finds high quality solutions.

关键词： Medical data classification Machine learning Feature selection Binary firefly algorithm Support vector machine (SVM) Cross-validation

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Heuristic for Context Detection in Time Varying Domains 3

Heuristic for Context Detection in Time Varying Domains

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3rd international conference on Frontiers in Intelligent Computing - Theory and applications (FICTA)

作者： Dandu, Sujatha Deekshatulu, B. L. Auroras Technol & Res Inst Hyderabad Andhra Pradesh India IDRBT Hyderabad Andhra Pradesh India

ISBN: (纸本)9783319119328

The primary goal is to find information of co-existing commodities called itemsets in transactional databases. Especially in business to make a proper decision, the knowledge of high support itemset is very important. For example: A business can avoid giving discounts on an item which is more in demand though it is one of the commodities of the same itemset. This phenomenal product's information must be known to data analyst. As we see the change becomes mandatory as the season changes. So it has a great effect on buying habits of customers, not only on the season but also newly introduced merchandise [2, 5, 6]. The primary duty of data analyst is to detect these changes i,e which high-support itemsets withstand the change and which itemset among itself vanishes and which new itemsets emerge. To take this challenge we use a window of the latest market-basket which shows variation in its size time to time. The window grows in the periods of stability, producing an information of the current context. The window reduces in size, once the change is detected. The main objective of this paper is to introduce a new operator for controlling the window size.

关键词： data stream mining frequent itemsets context change

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Constrained Big data Mining in an Edge Computing Environment 4th

Constrained Big Data Mining in an Edge Computing Environment

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4th international conference on Big data applications and Services ( (BigDAS)

作者： Leung, Carson K. Deng, Deyu Hoi, Calvin S. H. Lee, Wookey Univ Manitoba Winnipeg MB Canada Inha Univ Incheon South Korea

ISBN: (纸本)9789811306952;9789811306945

High volumes of wide varieties of valuable data of different veracity can be easily generated or collected at a high velocity from various big data applications and services. A rich source of these big data is the Internet of Things (IoT), which can be viewed as a network of sensors, mobile devices, wearable devices, and other "things" that are capable to operate within the existing Internet infrastructure. As a popular data science task, frequent pattern mining aims to discover implicit, previously unknown and potentially useful information and valuable knowledge-in terms of sets of frequently co-occurring items-embedded in these big data. Existing frequent pattern mining algorithms mostly run serially on a single local computer or in distributed and parallel environments on computer clusters, grids, or clouds. Many of these algorithms return large numbers of frequent patterns, of which only some may be of interest to the user. In this paper, we present a constrained big data mining algorithm that (i) focuses the mining to those frequent patterns that are interested to the users and (ii) runs in an edge computing environment, in which computation is performed at edges of the computing network.

关键词： Big data Big data applications and services data analytics data mining data science Edge computing Frequent patterns knowledge discovery in databases

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11th international conference on Practical applications of Computational Biology & Bioinformatics 1st ed. 2017

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丛书名： advances in Intelligent Systems and Computing

2017年

作者： Florentino Fdez-Riverola Mohd Saberi Mohamad Miguel Rocha Juan F. De Paz Tiago Pinto

ISBN: (数字)9783319608167

ISBN: (纸本)9783319608150

Biological and biomedical research are increasingly driven by experimental techniques that challenge our ability to analyse, process and extract meaningful knowledge from the underlying data. The impressive capabilities of next-generation sequencing technologies, together with novel and constantly evolving, distinct types of omics data technologies, have created an increasingly complex set of challenges for the growing fields of Bioinformatics and Computational Biology. The analysis of the datasets produced and their integration call for new algorithms and approaches from fields such as databases, Statistics, data Mining, Machine Learning, Optimization, Computer Science and Artificial Intelligence. Clearly, Biology is more and more a science of information and requires tools from the computational sciences. In the last few years, we have seen the rise of a new generation of interdisciplinary scientists with a strong background in the biological and computational sciences. In this context, the interaction of researchers from different scientific fields is, more than ever, of foremost importance in boosting the research efforts in the field and contributing to the education of a new generation of Bioinformatics scientists. The PACBB17 conference was intended to contribute to this effort and promote this fruitful interaction, with a technical program that included 39 papers spanning many different sub-fields in Bioinformatics and Computational Biology. Further, the conference promoted the interaction of scientists from diverse research groups and with a distinct background (computer scientists, mathematicians, biologists).

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8th international conference on Practical applications of Computational Biology &Bioinformatics (PACBB 2014) 1

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丛书名： advances in Intelligent Systems and Computing

2014年

作者： Julio Saez-Rodriguez, Miguel P. Rocha, Florentino Fdez-Riverola, Juan F. De Paz Santana (eds.)

ISBN: (数字)9783319075815

ISBN: (纸本)9783319075808;9783319075815

Biological and biomedical research are increasingly driven by experimental techniques that challenge our ability to analyse, process and extract meaningful knowledge from the underlying data. The impressive capabilities of next generation sequencing technologies, together with novel and ever evolving distinct types of omics data technologies, have put an increasingly complex set of challenges for the growing fields of Bioinformatics and Computational Biology. The analysis of the datasets produced and their integration call for new algorithms and approaches from fields such as databases, Statistics, data Mining, Machine Learning, Optimization, Computer Science and Artificial Intelligence. Clearly, Biology is more and more a science of information requiring tools from the computational sciences. In the last few years, we have seen the surge of a new generation of interdisciplinary scientists that have a strong background in the biological and computational sciences. In this context, the interaction of researchers from different scientific fields is, more than ever, of foremost importance boosting the research efforts in the field and contributing to the education of a new generation of Bioinformatics scientists. PACBB14 contributes to this effort promoting this fruitful interaction. PACBB'14 technical program included 34 papers spanning many different sub-fields in Bioinformatics and Computational Biology. Therefore, the conference promotes the interaction of scientists from diverse research groups and with a distinct background such as computer scientists, mathematicians or biologists.

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Analysis of Educational data Mining 4th

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4th international conference on Harmony Search, Soft Computing and applications (ICHSA)

作者： Ahuja, Ravinder Jha, Animesh Maurya, Rahul Srivastava, Rishabh Jaypee Inst Informat Technol Noida India

ISBN: (纸本)9789811307614;9789811307607

This research paper aims to compare the performance of various clustering and classification algorithms which are applied on the same educational dataset. Educational data Mining (EDM) uses these algorithms to explore educational statistics to discover patterns and predictions in data that illustrate learner's performance. Various design challenges such as accuracy, objective and functionality, and overheads when the data set is extremely large, etc., have been highlighted. The algorithms discussed here can be classified as centroid-based clustering, graph-based clustering, and various supervised classification algorithms. Further, after comparison of these algorithms, this paper aims at using the cited literature survey to determine the most suited algorithm according to the need of EDM clustering or classification. The regular search is on to understand students better and to know the patterns in which they learn to make it more efficient for them. Nowadays, many educational institutions have educational databases that can be utilized in various ways to make it effective for students but are unutilized. Powerful tools are required to get benefits from these educational databases. EDM is one of those emerging tools that analyzes the data collected from learning and teaching and then applies the techniques from machine learning and datamining for predicting student's future behavior by learning detailed information such as student's grades, knowledge, achievements, motivation, and attitude.

关键词： Clustering methods data mining Educational mining Analytic study Educational data

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A Novel Heuristic Approach for the Simultaneous Selection of the Optimal Clustering Method and Its Internal Parameters for Time Series data 14th

A Novel Heuristic Approach for the Simultaneous Selection of...

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14th international conference on Soft Computing Models in Industrial and Environmental applications (SOCO)

作者： Navajas-Guerrero, Adriana Manjarres, Diana Portillo, Eva Landa-Torres, Itziar Tecnalia Res & Innovat Parque Tecnol Bizkaia Derio 48160 Spain Univ Basque Country UPV EHU Sch Engn Dept Automat Control & Syst Engn Bilbao 48013 Spain Petronor Innovac SL Muskiz 48550 Spain

ISBN: (纸本)9783030200558;9783030200541

Clustering methods have become popular in the last years due to the need of analyzing the high amount of collected data from different fields of knowledge. Nevertheless, the main drawback of clustering is the selection of the optimal method along with its internal parameters in an unsupervised environment. In the present paper, a novel heuristic approach based on the Harmony Search algorithm aided with a local search procedure is presented for simultaneously optimizing the best clustering algorithm (K-means, DBSCAN and Hierarchical clustering) and its optimal internal parameters based on the Silhouette index. Extensive simulation results show that the presented approach outperforms the standard clustering configurations and also other works in the literature in different Time Series and synthetic databases.

关键词： Harmony Search Clustering Internal parameters configuration Optimization Time series clustering

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Selection of Evolutionary Approach based Hybrid data Mining Algorithms for Decision Support Systems and Business Intelligence 12

Selection of Evolutionary Approach based Hybrid Data Mining ...

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international conference on advances in Computing, Communications and Informatics (ICACCI)

作者： Kumar, Pardeep Nitin Sehgal, Vivek Kumar Chauhan, Durg Singh Jaypee Univ Informat Technol CSE & ICT Dept Solan HP India

ISBN: (纸本)9781450311960

The business intelligence is constantly changing, and it is becoming more complex. Organizations, private and public, are under pressures that force them to respond quickly to changing conditions and to be innovative in the way they operate. Such activities require organizations to be agile and to make frequent and quick strategic, tactical, and operational decisions. In today's era, we observe major changes in how managers use computerized support in making decisions. As more number of decision-makers become computer literate, decision support systems (DSS) is evolving from its beginning as a personal support tool and is becoming the shared resource in an organization. data mining has been an active area of research in last two decades. Integration of data mining and decision support systems (DSS) can lead to the improved performance and can enable the tackling of new types of problems. In the recent past, there has been an increasing interest in applying evolutionary methods to knowledge Discovery in databases (KDD) and a number of successful applications of Genetic Algorithms (GA) and Genetic Programming (GP) to KDD have been demonstrated. No single algorithm has been found to be superior over all others for all data sets. This paper sheds some light on the selection of evolutionary approach based hybrid classification models in diversity of datasets from different domains. NNEP-C (s), XCS-C (s) GFS-GP-C(s) evolved from the combination of genetic algorithm and other techniques as neural network, self evolving GA and fuzzy learning have been tested on five datasets based on selected quality measures like predictive accuracy and training time. XCS-C(s) shows faster speed as compared to its competitor NNEP-C(s). GFS-GP-C(s) is the slowest one.

关键词： Decision Support Systems Hybrid Models knowledge Discovery in databases Genetic Programming Predictive Accuracy Training Time

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