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.

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.

scholarly journals EDM – survey of performance factors and algorithms applied

2018 ◽  
Vol 7 (2.6) ◽  
pp. 93 ◽  
Author(s):  
Deepali R Vora ◽  
Kamatchi Iyer
Keyword(s):  
Data Mining ◽  
Information Systems ◽  
Knowledge Discovery ◽  
Association Rules ◽  
Educational Data Mining ◽  
Knowledge Discovery In Databases ◽  
Useful Knowledge ◽  
Performance Factors ◽  
Student’S Performance

Educational Data Mining (EDM) is a new field of research in the data mining and Knowledge Discovery in Databases (KDD) field. It mainly focuses in mining useful patterns and discovering useful knowledge from the educational information systems from schools, to colleges and universities. Analysing students’ data and information to perform various tasks like classification of students, or to create decision trees or association rules, so as to make better decisions or to enhance student’s performance is an interesting field of research. The paper presents a survey of various tasks performed in EDM and algorithms (methods) used for the same. The paper identifies the lacuna and challenges in Algorithms applied, Performance Factors considered and data used in EDM.

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.

Credit Risk Assessment and Data Mining

2011 ◽  
pp. 800-805 ◽  
Author(s):  
André Carlos Ponce de Leon Ferreira de Carvalho ◽  
João Manuel Portela Gama ◽  
Teresa Bernarda Ludermir
Keyword(s):  
Data Mining ◽  
Credit Risk ◽  
Financial Management ◽  
Computational Tools ◽  
Useful Knowledge ◽  
Efficient Data ◽  
Structured Problems ◽  
Computer Based ◽  
Credit Risk Analysis ◽  
Political Economic

The widespread use of databases and the fast increase of the volume of data they store are creating a problem and a new opportunity for credit companies. These companies are realizing the necessity of making an efficient use of the information stored in their databases, extracting useful knowledge to support their decision-making process. Nowadays, knowledge is the most valuable asset a company or nation may have. Several companies are investing large sums of money in the development of new computational tools able to extract meaningful knowledge from large volumes of data collected over many years. Among such companies, companies working with credit risk analysis have invested heavily in sophisticated computational tools to perform efficient data mining in their databases. The behavior of the financial market is affected by a large number of political, economic, and psychological factors, which are correlated and interact among themselves in a complex way. The majority of these relations seems to be probabilistic and non-linear. Thus, these relations are hard to express through deterministic rules. Simon (1960) classifies the financial management decisions in a continuous interval, whose limits are non-structure and highly structured. The highly structured decisions are those where the processes necessary for the achievement of a good solution are known beforehand and several computational tools to support the decisions are available. For non-structured decisions, only the managers’ intuition and experience are used. Specialists may support these managers, but the final decisions involve a substantial amount of subjective elements. Highly non-structured problems are not easily adapted to the computer-based conventional analysis methods or decision support systems (Hawley, Johnson, & Raina, 1996).

Fuzzy Methods in Data Mining

2011 ◽  
pp. 907-912 ◽  
Author(s):  
Eyke Hüllermeier
Keyword(s):  
Data Mining ◽  
Knowledge Acquisition ◽  
Control Function ◽  
Knowledge Discovery In Databases ◽  
Knowledge Based ◽  
Automated Learning ◽  
Long Time ◽  
Basic Ideas ◽  
Inference Techniques ◽  
Tools And Techniques

Tools and techniques that have been developed during the last 40 years in the field of fuzzy set theory (FST) have been applied quite successfully in a variety of application areas. A prominent example of the practical usefulness of corresponding techniques is fuzzy control, where the idea is to represent the input-output behaviour of a controller (of a technical system) in terms of fuzzy rules. A concrete control function is derived from such rules by means of suitable inference techniques. While aspects of knowledge representation and reasoning have dominated research in FST for a long time, problems of automated learning and knowledge acquisition have more and more come to the fore in recent years. There are several reasons for this development, notably the following: Firstly, there has been an internal shift within fuzzy systems research from “modelling” to “learning”, which can be attributed to the awareness that the well-known “knowledge acquisition bottleneck” seems to remain one of the key problems in the design of intelligent and knowledge-based systems. Secondly, this trend has been further amplified by the great interest that the fields of knowledge discovery in databases (KDD) and its core methodical component, data mining, have attracted in recent years. It is hence hardly surprising that data mining has received a great deal of attention in the FST community in recent years (Hüllermeier, 2005). The aim of this chapter is to give an idea of the usefulness of FST for data mining. To this end, we shall briefly highlight, in the next but one section, some potential advantages of fuzzy approaches. In preparation, the next section briefly recalls some basic ideas and concepts from FST. The style of presentation is purely non-technical throughout; for technical details we shall give pointers to the literature.

Sign in / Sign up

Export Citation Format

Share Document