scholarly journals Using Meta-mining to Support Data Mining Workflow Planning and Optimization

2014 ◽  
Vol 51 ◽  
pp. 605-644 ◽  
Author(s):  
P. Nguyen ◽  
M. Hilario ◽  
A. Kalousis
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Performance Optimization ◽  
Domain Knowledge ◽  
Performance Measure ◽  
Alternative Methods ◽  
Knowledge Discovery In Databases ◽  
Similarity Learning ◽  
Real World Datasets ◽  
Workflow Planning

Knowledge Discovery in Databases is a complex process that involves many different data processing and learning operators. Today's Knowledge Discovery Support Systems can contain several hundred operators. A major challenge is to assist the user in designing workflows which are not only valid but also -- ideally -- optimize some performance measure associated with the user goal. In this paper we present such a system. The system relies on a meta-mining module which analyses past data mining experiments and extracts meta-mining models which associate dataset characteristics with workflow descriptors in view of workflow performance optimization. The meta-mining model is used within a data mining workflow planner, to guide the planner during the workflow planning. We learn the meta-mining models using a similarity learning approach, and extract the workflow descriptors by mining the workflows for generalized relational patterns accounting also for domain knowledge provided by a data mining ontology. We evaluate the quality of the data mining workflows that the system produces on a collection of real world datasets coming from biology and show that it produces workflows that are significantly better than alternative methods that can only do workflow selection and not planning.

scholarly journals Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Shadi Aljawarneh ◽  
Aurea Anguera ◽  
John William Atwood ◽  
Juan A. Lara ◽  
David Lizcano
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Knowledge Discovery ◽  
Time Series Data ◽  
Medical Patient ◽  
Lessons Learned ◽  
Physiological Signals ◽  
Knowledge Discovery In Databases ◽  
Series Data ◽  
Data Mining Techniques

AbstractNowadays, large amounts of data are generated in the medical domain. Various physiological signals generated from different organs can be recorded to extract interesting information about patients’ health. The analysis of physiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery in Databases process. The application of such process in the domain of medicine has a series of implications and difficulties, especially regarding the application of data mining techniques to data, mainly time series, gathered from medical examinations of patients. The goal of this paper is to describe the lessons learned and the experience gathered by the authors applying data mining techniques to real medical patient data including time series. In this research, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15 professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epileptic patients). We applied a previously proposed knowledge discovery framework for classification purpose obtaining good results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in our research are the groundwork for the lessons learned and recommendations made in this position paper that intends to be a guide for experts who have to face similar medical data mining projects.

scholarly journals Proposta de metodologia para a criação de etiqueta de classificação – estudo de caso: desempenho escolar

2016 ◽  
Vol 23 (1) ◽  
pp. 177-191
Author(s):  
Anderson Roges Teixeira Góes ◽  
Maria Teresinha Arns Steiner
Keyword(s):  
Data Mining ◽  
Support Vector Machines ◽  
Knowledge Discovery ◽  
Knowledge Discovery In Databases ◽  
Support Vector ◽  
Vector Machines

Resumo A qualidade na educação tem sido objeto de muita discussão, seja nas escolas e entre seus gestores, seja na mídia ou na literatura. No entanto, uma análise mais profunda na literatura parece não indicar técnicas que explorem bancos de dados com a finalidade de obter classificações para o desempenho escolar, nem tampouco há um consenso sobre o que seja “qualidade educacional”. Diante deste contexto, neste artigo, é proposta uma metodologia que se enquadra no processo KDD (Knowledge Discovery in Databases, ou seja, Descoberta de Conhecimento em Bases de Dados) para a classificação do desempenho de instituições de ensino, de forma comparativa, com base nas notas obtidas na Prova Brasil, um dos itens integrantes do Índice de Desenvolvimento da Educação Básica (IDEB) no Brasil. Para ilustrar a metodologia, esta foi aplicada às escolas públicas municipais de Araucária, PR, região metropolitana de Curitiba, PR, num total de 17, que, por ocasião da pesquisa, ofertavam Ensino Fundamental, considerando as notas obtidas pela totalidade dos alunos dos anos iniciais (1º. ao 5º. ano do ensino fundamental) e dos anos finais (6º. ao 9º. ano do ensino fundamental). Na etapa de Data Mining, principal etapa do processo KDD, foram utilizadas três técnicas de forma comparativa para o Reconhecimento de Padrões: Redes Neurais Artificiais; Support Vector Machines; e Algoritmos Genéticos. Essas técnicas apresentaram resultados satisfatórios na classificação das escolas, representados por meio de uma “Etiqueta de Classificação do Desempenho”. Por meio desta etiqueta, os gestores educacionais poderão ter melhor base para definir as medidas a serem adotadas junto a cada escola, podendo definir mais claramente as metas a serem cumpridas.

Data Mining and Knowledge Discovery in Databases

2019 ◽  
pp. 502-514 ◽  
Author(s):  
Ana Azevedo
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Knowledge Discovery In Databases ◽  
Great Relevance ◽  
The Subject ◽  
Inductive Databases ◽  
High Level

The term knowledge discovery in databases or KDD, for short, was coined in 1989 to refer to the broad process of finding knowledge in data, and to emphasize the “high-level” application of particular data mining (DM) methods. The DM phase concerns, mainly, the means by which the patterns are extracted and enumerated from data. Nowadays, the two terms are, usually, indistinctly used. Efforts are being developed in order to create standards and rules in the field of DM with great relevance being given to the subject of inductive databases. Within the context of inductive databases, a great relevance is given to the so-called DM languages. This chapter explores DM in KDD.

Data Mining and Knowledge Discovery

2011 ◽  
pp. 35-47 ◽  
Author(s):  
Andi Baritchi
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Business Processes ◽  
Knowledge Discovery In Databases ◽  
Business World ◽  
Useful Knowledge ◽  
Customer Support ◽  
Customer Base ◽  
Sales And Marketing ◽  
Customer Profiling

In today’s business world, the use of computers for everyday business processes and data recording has become virtually ubiquitous. With the advent of this electronic age comes one priceless by-product — data. As more and more executives are discovering each day, companies can harness data to gain valuable insights into their customer base. Data mining is the process used to take these immense streams of data and reduce them to useful knowledge. Data mining has limitless applications, including sales and marketing, customer support, knowledge-base development, not to mention fraud detection for virtually any field, etc. “Data mining,” a bit of a misnomer, refers to mining the data to find the gems hidden inside the data, and as such it is the most often-used reference to this process. It is important to note, however, that data mining is only one part of the Knowledge Discovery in Databases process, albeit it is the workhorse. In this chapter, we provide a concise description of the Knowledge Discovery process, from domain analysis and data selection, to data preprocessing and transformation, to the data mining itself, and finally the interpretation and evaluation of the results as applied to the domain. We describe the different flavors of data mining, including association rules, classification and prediction, clustering and outlier analysis, customer profiling, and how each of these can be used in practice to improve a business’ understanding of its customers. We introduce the reader to some of today’s hot data mining resources, and then for those that are interested, at the end of the chapter we provide a concise technical overview of how each data-mining technology works.

Translating Advances in Data Mining in Business Operations

2008 ◽  
pp. 2734-2748
Author(s):  
Henry Dillon ◽  
Beverley Hope
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Data Analysis ◽  
Knowledge Discovery ◽  
Knowledge Discovery In Databases ◽  
Business Context ◽  
Business Operations ◽  
Analysis Tools ◽  
It Managers ◽  
Tools And Techniques

Knowledge discovery in databases (KDD) is a field of research that studies the development and use of various data analysis tools and techniques. KDD research has produced an array of models, theories, functions and methodologies for producing knowledge from data. However, despite these advances, nearly two thirds of information technology (IT) managers say that data mining products are too difficult to use in a business context. This chapter discusses how advances in data mining translate into the business context. It highlights the art of business implementation rather than the science of KDD.

Expression and Processing of Inductive Queries

2009 ◽  
pp. 518-526
Author(s):  
Edgard Benítez-Guerrero ◽  
Omar Nieva-García
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Pattern Discovery ◽  
Data Preprocessing ◽  
Knowledge Discovery In Databases ◽  
Digital Information ◽  
Interactive Process ◽  
Useful Knowledge ◽  
Knowledge Based ◽  
Computer Based

The vast amounts of digital information stored in databases and other repositories represent a challenge for finding useful knowledge. Traditionalmethods for turning data into knowledge based on manual analysis reach their limits in this context, and for this reason, computer-based methods are needed. Knowledge Discovery in Databases (KDD) is the semi-automatic, nontrivial process of identifying valid, novel, potentially useful, and understandable knowledge (in the form of patterns) in data (Fayyad, Piatetsky-Shapiro, Smyth & Uthurusamy, 1996). KDD is an iterative and interactive process with several steps: understanding the problem domain, data preprocessing, pattern discovery, and pattern evaluation and usage. For discovering patterns, Data Mining (DM) techniques are applied.

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