Study of the polymorphism of the PatagonianCalceolaria polyrhiza(Calceolariaceae) using decision tree and sequential covering rule induction

2013 ◽  
Vol 173 (3) ◽  
pp. 487-500 ◽  
Author(s):  
Marina M. Strelin ◽  
Andrea Cosacov ◽  
Martin Diller ◽  
Alicia N. Sérsic
Keyword(s):  
Decision Tree ◽  
Rule Induction ◽  
Sequential Covering

Identification Through Data Mining

2011 ◽  
pp. 374-381
Author(s):  
Diego Liberati
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Bayesian Networks ◽  
Time Course ◽  
Minimum Description Length ◽  
General Purpose ◽  
Rule Induction ◽  
Induction Method ◽  
Principal Directions ◽  
Complementary Techniques

Four main general purpose approaches inferring knowledge from data are presented as a useful pool of at least partially complementary techniques also in the cyber intrusion identification context. In order to reduce the dimensionality of the problem, the most salient variables can be selected by cascading to a K-means a Divisive Partitioning of data orthogonal to the Principal Directions. A rule induction method based on logical circuits synthesis after proper binarization of the original variables proves to be also able to further prune redundant variables, besides identifying logical relationships among them in an understandable “if . then ..” form. Adaptive Bayesian networks are used to build a decision tree over the hierarchy of variables ordered by Minimum Description Length. Finally, Piece-Wise Affine Identification also provides a model of the dynamics of the process underlying the data, by detecting possible switches and changes of trends on the time course of the monitoring.

Study of population dynamics of soybean semi-looper Gesonia gemmaSwinhoe by using rule induction model in Maharashtra, India

2017 ◽  
Author(s):  
J. Cruz Antony ◽  
M. Pratheepa
Keyword(s):  
Population Dynamics ◽  
Control Strategy ◽  
Rule Induction ◽  
Maximum Temperature ◽  
Classification Rules ◽  
Target Feature ◽  
Sequential Covering ◽  
Heavy Damage ◽  
Induction Model ◽  
Rainy Days

Gesonia gemma Swinhoe (1885) is a grey semi-looper and it has emerged as a serious threat to the soybean crop. This defoliator causes heavy damage to the crop in the form of loss in grain weight. Gesonia gemma population dynamics was studied in various districts of Maharashtra. Sequential covering algorithm (CN2 rule induction) has been proposed for rule induction model to generate a list of classification rules with target feature (G. gemma population) and the independent abiotic features. The classification rules have exhibited more accuracy and showed that maximum temperature and humidity with less number of rainy days has influenced the population of Gesonia gemma in Maharashtra. Hence, this rule induction model can be used to study the collected evidence for prediction and it will be helpful to the farmers to take necessary pest control strategy.

scholarly journals Prediksi Ketepatan Kelulusan Mahasiswa Diploma dengan Komparasi Algoritma Klasifikasi

2019 ◽  
Vol 7 (3) ◽  
pp. 202
Author(s):  
Muhammad Sony Maulana ◽  
Raja Sabarudin ◽  
Wahyu Nugraha
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Rule Induction ◽  
T Test

AMIK BSI Pontianak merupakan salah satu perguruan tinggi swasta yang memiliki jumlah mahasiswa yang banyak, namun dalam perjalanannya masih terdapat permasalahan yang setiap tahun nya terjadi yaitu permasalahan jumlah kelulusan mahasiswa yang tepat waktu dan terlambat. Jumlah mahasiswa yang lulus tepat waktu menjadi indikator efektifitas dari sebuah perguruan tinggi baik negeri dan swasta. Perguruan tinggi perlu mendeteksi perilaku  dari mahasiswa aktif sehingga dapat dilihat faktor yang menyebabkan mahasiswa tidak lulus tepat waktu. Pada penelitian ini, akan mengkomparasikan atau membandingkan 5 metode data mining untuk menentukan metode mana yang paling optimal dalam menentukan ketepatan kelulusan mahasiswa dengan teknik pengujian T-Test, metode yang dibandingkan adalah metode Decision Tree, Naive Bayes, K-NN, Rule Induction, dan Random Forest. Hasil dari penelitian ini menghasilkan bahwa algoritma Rule Induction dan C4.5 adalah metode yang paling optimal performanya dalam menentukan ketepatan kelulusan mahasiswa diploma AMIK BSI Pontianak

Model Identification Through Data Mining

2008 ◽  
pp. 2281-2288
Author(s):  
Diego Liberati
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Bayesian Networks ◽  
Time Course ◽  
Minimum Description Length ◽  
Model Identification ◽  
General Purpose ◽  
Rule Induction ◽  
Principal Directions ◽  
Complementary Techniques

Four main general purpose approaches inferring knowledge from data are presented as a useful pool of at least partially complementary techniques also in the cyber intrusion identification context. In order to reduce the dimensionality of the problem, the most salient variables can be selected by cascading to a K-means a Divisive Partitioning of data orthogonal to the Principal Directions. A rule induction method based on logical circuits synthesis after proper binarization of the original variables proves to be also able to further prune redundant variables, besides identifying logical relationships among them in an understandable “if . then ..” form. Adaptive Bayesian networks are used to build a decision tree over the hierarchy of variables ordered by Minimum Description Length. Finally, Piece-Wise Affine Identification also provides a model of the dynamics of the process underlying the data, by detecting possible switches and changes of trends on the time course of the monitoring.

Sequential covering rule induction algorithm for variable consistency rough set approaches

2011 ◽  
Vol 181 (5) ◽  
pp. 987-1002 ◽  
Author(s):  
Jerzy Błaszczyński ◽  
Roman Słowiński ◽  
Marcin Szeląg
Keyword(s):  
Rough Set ◽  
Rule Induction ◽  
Sequential Covering
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