scholarly journals Knowledge Discovery Through Time Series Applied to Students' Grades

2020 ◽  
Author(s):  
Joaquim Assunção ◽  
Fernando Oliveira ◽  
Claiton Correa
Keyword(s):  
High School ◽  
Data Mining ◽  
Time Series ◽  
Knowledge Discovery ◽  
School System ◽  
Historical Data ◽  
Traditional Approach ◽  
Time Series Modeling ◽  
Institute Of Technology ◽  
Teaching Learning

Assessment is a constant activity in education, in the school system, and the teaching-learning process. The traditional approach classifies the students learning level through grades. This paper shows an application of knowledge discovery and data mining through classification and clustering via time series modeling on students' grades from high school. We collected historical data from an institute of technology, from this, we created models that can be used to extract patterns to help teachers to understand the profile of the students and provide early warns about possible poor results.

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 Uma metodologia para a descoberta de conhecimento em bases de dados da Prova BrasilA methodology for knowledge discovering in Prova Brasil databases

2018 ◽  
Vol 20 (2) ◽  
Author(s):  
Stella Oggioni da Fonseca ◽  
Adriana Da Rocha Silva ◽  
Anderson Amendoeira Namen
Keyword(s):  
Data Mining ◽  
Elementary Education ◽  
Knowledge Discovery ◽  
Federal Government ◽  
Learning Process ◽  
Reduction Process ◽  
Educational Environment ◽  
Knowledge Discovery In Databases ◽  
Mathematics Tests ◽  
Teaching Learning

A Prova Brasil é uma avaliação que, por intermédio da aplicação de testes e questionários, coleta informações sobre o ensino fundamental. O presente trabalho objetiva apresentar uma metodologia capaz de identificar aspectos, relacionados ao ambiente educacional, que possam ter influenciado positiva ou negativamente no resultado obtido pelos alunos nos testes de Matemática, aplicados em 2013. A abordagem proposta consiste, essencialmente, de um processo de redução de dimensionalidade com posterior aplicação de mineração de dados visando à descoberta de conhecimento nas bases. A partir das conclusões obtidas é possível fomentar a discussão que busquem o alcance de melhorias no processo de ensino-aprendizagem, bem como estimular pesquisas acerca dos dados disponibilizados pelo Governo Federal.Prova Brasil is an evaluation that, through the application of tests and questionnaires, collects information about elementary education. The present work aims to present a methodology for the extraction of aspects related to the educational environment that may have influenced positively or negatively students’ results in the Mathematics tests applied in 2013. The proposed approach consists of a dimensionality reduction process followed by data mining, aiming to get knowledge discovery in databases. Based on the conclusions obtained, discussions about actions for improvements in the teaching-learning process can be made, as well the fostering of researches on the data provided by the Federal Government.

scholarly journals A DESCRIPTIVE FRAMEWORK FOR THE FIELD OF DATA MINING AND KNOWLEDGE DISCOVERY

2008 ◽  
Vol 07 (04) ◽  
pp. 639-682 ◽  
Author(s):  
YI PENG ◽  
GANG KOU ◽  
YONG SHI ◽  
ZHENGXIN CHEN
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Rapid Development ◽  
Theory Approach ◽  
Research Activities ◽  
Comprehensive Picture ◽  
Teaching Learning ◽  
High Level ◽  
Descriptive Framework ◽  
Grounded Theory Approach

Despite the rapid development, the field of data mining and knowledge discovery (DMKD) is still vaguely defined and lack of integrated descriptions. This situation causes difficulties in teaching, learning, research, and application. This paper surveys a large collection of DMKD literature to provide a comprehensive picture of current DMKD research and classify these research activities into high-level categories using grounded theory approach; it also evaluates the longitudinal changes of DMKD research activities during the last decade.

Algorithms of Data Mining and Knowledge Discovery of Correlativity in Two-Dimensional Time Series

2012 ◽  
Vol 263-266 ◽  
pp. 1844-1848
Author(s):  
Shao Lin Hu ◽  
Ye Li ◽  
Wei Zhang
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Knowledge Discovery ◽  
Stationary Time Series ◽  
Two Dimensional ◽  
Qualitative Knowledge ◽  
Model Set ◽  
Professional Fields ◽  
Mining Algorithms ◽  
New Algorithms

Oriented at dynamic data from complicated process with noise disturbance, it is very difficult to discover knowledge of correlativity and orderliness. Following some analyzing results about the shortcoming of relative coefficients in mining non-stationary time series, a series of new algorithms are built in this paper to mine correlativity in two-dimensional time series. These new algorithms are based on a expansible framework of model set. Based on these new mining algorithms, a making decision table is listed not only to mine correlativity in two-dimensional time series, but also to discover deepening knowledge to transform the qualitative knowledge “nonlinear relativity” as well as “non-relativity” into deeper quantitative knowledge about analytical model. These new approaches given in this paper is exoteric in framework and can be enriched with additional new models. In this way, some professional data mining and knowledge discovery cab be fulfilled to aim at some specific professional fields.

scholarly journals Penerapan Metode Clustering Dengan Algoritma K-Means Tindak Kejahatan Pencurian di Kabupaten Asahan

2021 ◽  
Vol 1 (1) ◽  
pp. 7-14
Author(s):  
Nur Afni Syahpitri Damanik ◽  
Irianto Irianto ◽  
Dahriansah Dahriansah
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Historical Data ◽  
Large Data ◽  
Large Data Sets ◽  
Annual Data ◽  
Data Sets ◽  
Process Data ◽  
Different Characteristics

Abstract:Theft is the illegal taking of property or belongings of another person without the permission of the owner. The most common crime problem in Asahan District is theft, so that the POLRES is still having trouble determining which areas are often the crime of theft. With this problem, we need to do a grouping for areas where theft often occurs, so the process used  is the data mining process. Data mining is one of the processes of Knowledge Discovery from Databases (KDD). KDD is an activity that includes collecting, using historical data to find regularities, patterns or relationships in large data sets. One of the techniques known in data mining is clustering technique. The K-Means method is a method for clustering techniques, K- Means is a method that partitions data into groups so that data with the same characteristics are entered into the same set of groups and data with different characteristics are grouped into other groups. The attributes used in grouping this data are annual data, namely 2015, 2016, 2017, 2018, 2019. A case study of 9 POLSEK in the Asahan. Keywords: Data Mining, Clustering, K-Means Algorithm, Theft Crimes Grouping.  Abstrak: Pencurian merupakan pengambilan properti atau barang milik orang lain secara tidak sah tanpa ijin dari pemilik. Masalah tindak kejahatan yang paling banyak terjadi di Kabupaten Asahan adalah tindak kejahatan pencurian sehingga pihak POLRES masih kesulitan untuk menentukan daerah mana saja yang sering terjadi tindak kejahatan pencuriaan. Dengan adanya masalah ini kita perlu melakukan pengelompokan untuk daerah mana saja yang sering terjadi tindak pencurian maka proses yang digunakan adalah proses data mining. Data mining adalah salah satu proses dari Knowledge Discovery from Databases (KDD). KDD adalah kegiatan yang meliputi pengumpulan, pemakaian data, historis untuk menemukan keteraturan, pola atau hubungan dalam set data besar. Salah satu teknik yang di kenal dalam data mining adalah teknik clustering. Metode K-Means merupakan metode untuk teknik clustering, K-Means adalah metode yang mempartisi data kedalam kelompok sehingga data berkarakteristik sama dimasukan kedalam set kelompok yang sama dan data yang berkerakteristik berbeda dikelompokkan ke dalam kelompok yang lain. Atribut yang di gunakan dalam pengelomokan data ini adalah data pertahun yaitu tahun 2015, 2016, 2017, 2018, 2019. Studi kasus pada 9 POLSEK yang ada di daerah kabupaten Asahan. Kata kunci: Data Mining, Clustering, Algoritma K-Means, Pengelompokan Tindak Kejahatan  Pencurian.

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