Orange
2 нояб. 2017 г. 1. Data Mining технология не может заменить аналитика. 2. Технология не может дать ответы на те вопросы которые не были заданы. 3.
Orange: Data Mining Toolbox in Python
Orange is a machine learning and data mining suite for data analysis through Python scripting and visual programming. Here we report on the scripting part
My experience with PostgreSQL and Orange in data mining
data = Orange.data.Table("voting") classifier = Orange.classification.LogisticRegressionLearner(data) c_values = data.domain.class_var.values for d in data[5:8]:.
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9 февр. 2018 г. В дальнейшем использовать пакет Orange. Данный пакет можно скачать по адресу: https://orange.biolab.si/. Лаборатория интернет-исследований ...
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Orange: Data Mining Toolbox in Python
Orange is a machine learning and data mining suite for data analysis through Python scripting and visual programming. Here we report on the scripting part
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Orange Data Mining - k-Means. Pagina 1 di 7 https://orange.biolab.si/widget-catalog/unsupervised/kmeans/ k-Means. Groups items using the k-Means clustering
Orange: Data Mining Toolbox in Python
Orange is a machine learning and data mining suite for data analysis through Python scripting and visual programming. Here we report on the scripting part
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Introduction to Data Mining
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Orange Software Usage in Data Mining. Classification Method on The Dataset Lenses. To cite this article: Aulia Ishak et al 2020 IOP Conf. Ser.: Mater. Sci.
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Orange: Data Mining Toolbox in Python
Orange is a machine learning and data mining suite for data analysis through Python scripting and visual programming. Here we report on the scripting part
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Orange Tweet Analysis Tutorial - hcommonsorg
Orange is an open-source data mining and analysis tool that uses widgets to create workflows to process the data We will be using the basic functionality of the program along with the Text add-on Orange is available for Windows Mac and Linux and the installation is straightforward Navigate to: https://orange biolab si/download
Introduction to Data Mining - filebiolabsi
Orange installation Orange can read data from spreadsheet ?le formats which include tab and comma separated and Excel ?les Let us prepare a data set (with school subjects and grades) in Excel and save it on a local disk In Orange we can use the File widget to load this data Looks ok Orange has correctly guessed that student names are
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Orange Data mining toolset (text images networks etc ) Workflow process Widget-based No programming is necessary You create workflows by connecting widgets If you have not already installed Orange and the Text Add-on please see the detailed directions in the PDF Pre-Processing Your Data
What is orange Data Mining Tool? - AskingLotcom
We will use Orange to construct visual data mining ?ows Many similar data mining environments exist but the organizers prefer Orange for a simple reason—they are its authors # If you haven’t already installed Orange please follow the installation guide at http://biolab github io/functional-genomics-workshop-orange #! 1 Data Mining
Comparative Study of Different Orange Data Mining - Springer
Orange data mining tool 1 Introduction Nowadays image classi?cation has taken the front position in different areas of research such as data mining computer vision medical image analysis arti?cial intelligence and so on [1] Figure 1 shows the rapid rise in the unstructured data S Mohapatra (&)
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[PDF] Introduction to Data Mining
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What is orange add-on for text mining?
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Orange Software Usage in Data Mining Classification Method on The Dataset LensesAulia Ishak1
, Khawarita Siregar2, Aspriyati3, Rosnani Ginting4, Muhammad Afif51,2,4,5Industrial Engineering Department, Faculty of Engineering, Universitas Sumatera Utara
3Public Health Faculty, Universitas Sumatera Utara, Medan, Indonesia
E-mail: muhammadafif2603@gmail.com aulia.ishak@usu.ac.idAbstract. Data Mining is a process that uses statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify useful information and related
knowledge from various large databases. Decision tree is a very interesting classification
method that involves the construction of a decision tree consisting of decision nodes which are connected by branches from the root node to the leaf node (end). The problem that will be examined in this study is about classification of dataset lenses obtained from using orange software. The method used in this paper is the method of classification process is performed ona decision tree using the orange software. Tree construction begins with the formation of roots (located at the top). Then the data is divided based on attributes that are suitable to be used as
leaves. Decision rule information is to make decision rules from trees that have been formed.1. Introduction
Data mining has attracted a lot of attention in the community in recent years, being able to convertlarge amounts and large amounts of data into useful information and knowledge. The information and knownledge obtained can be used to apply such as market analysis, fraud detection, and customer
retention, for production control and exploration science.Data mining is a process that uses statistical, mathematical, artificial intelligence, and machine
learning techniques to extract and identify useful information and related knowledge from variouslarge databases [1]. Data mining is a series of processes to explore the added value of a data set in the
form of knowledge that has not been knwn manually [2]. On the off chance that the consequences ofQC tests can't satisfy the acknowledgment models, the aftereffects of examination of the entire
arrangement of the estimations on that day must be eliminated or should be re-dissected, and an
incomplete or full re-approval of the strategy considered [12]. There are many data mining methodologies, one of which is poplar is the decision tree. Decision treeis a very interesting classification method that involves the construction of a decision tree consisting of
decision nodes which are connected by branches from the root node to the leaf node (end). At thedecision node the attribute will be tested, and each result will produce a branch. Each branch will be
directed to another node or to the end node to produce a decision [3]. 2 Contact lenses have become part of the lifestyle of modern society today. Contact lenses are verypopular, especially in big cities. Many people, especially women, use contact lenses not only as visual
aids but are also used as cosmetic tools to beautify the eye with a variety of attractive colors [4].
To build classification algorithms on datasets, open source data mining software is needed. The
software used is a tool from Orange based on phyton programming [5]. Orange is a data mining tool which is useful for visual programming and explorative data analysis. It can be written in phyton.Orange has multiple components are known as widgets. This data mining tool supports macOS,
Windows and Linux [6].
The decision tree has several versions, namely ID3, C4.5, J48, C5.0. However, Orange Data Mininguses ID3 which not only acts a classification, but can also perform regression, as in the CART method
[7]. The classification results of Decision Tree produce two rules of supplier selection model to
classify suppliers so thet companies can easily choose suppliers according to the criteria desired by the
company [8]The problem that will be examined in this study is about he classification of dataset lenses obtained
from using orange software. Where the dataset lenses is obtained from the UCI Machine LearningRepository website and is used to analyze contact pairs. By doing this research, is expected to produce
a knowledge for patients who require or do not require contact lenses.2. Research Methodology
The study was conducted using Orange software with classification method performed on the decision tree. The data used is secondary data, data collected second hand or from other sources that were available before the study was conducted [9]. The data source obtained comes from the UCI Machine Learning Repository website. The steps taken are entering the dataset to be checked into the Orangesoftware. After entering the dataset, then arrange the widgets in such a way as to install the decision
tree on Orange. By installing Tree Viewer, will bring up results obtained decision tree. The data used
in this study were taken from UCI with 24 data and 4 attributes.2.1. Attributes on Dataset
Attribute is something that is important to the accuracy of the procces, so it is necessary to know the
main attributes [10]. dataset include: The patient should be fitted with hard contact lenses (1) The patient should be fitted with soft contact lenses (2) The patient should not be fitted with contact lenses (3) And 4 other attributes as much as 4 pieces, namely: Age of the patient: (1) young, (2) pre-presbyopic, (3) presbopic Spectacle prescriptions: (1) myope, (2) hypermetropeAstigmatic: (1) no, (2) yes
Tear production rate: (1) reduced, (2) normal
2.2. Missing value on Dataset
Missing value is information that is not available for an object (case). The missing value due to the
information about the object is not given, it is difficult to find, or indeed the information is not ther, it
will cause a decrease in the accuracy and the quality of the data as it is processed [11]. Missing value
in the dataset is nothing (0).2.3. Instances on Dataset
Instances are the number of records in the dataset. The number of instances in the dataset is 24. 32.4. Characteristics of Attibutes on Dataset
The characteristic of the attributes in this dataset are chategorical.2.5. Dataset Display
Here is a table of dataset lenses.
Table 1. Dataset lenses display
No. Age of the
patientSpectacle
prescription Astigmatic Tear production rate Type1 1 1 1 1 3
2 1 1 1 2 2
3 1 1 2 1 3
4 1 1 2 2 1
5 1 2 1 1 3
6 1 2 1 2 2
7 1 2 2 1 3
8 1 2 2 2 1
9 2 1 1 1 3
10 2 1 1 2 2
11 2 1 2 1 3
12 2 1 2 2 1
13 2 2 1 1 3
14 2 2 1 2 2
15 2 2 2 1 3
16 2 2 2 2 3
17 3 1 1 1 3
18 3 1 1 2 3
19 3 1 2 1 3
20 3 1 2 2 1
21 3 2 1 1 3
22 3 2 1 2 2
23 3 2 2 1 3
24 3 2 2 2 3
In this study, expected after doing this research will produce a science that can be used for the benefit
of government and society.3. Result and Discussion
Here is an initial stage until the end of the classification decision tree by using Orange: Open the Orange software, then select File and drag it to the worksheet section 4Figure 1. Display widget file in orange software
Double-click the File symbol on the worksheet, and select the dataset to be examined. So the new window appears as shown below e Figure 2. Display file properties in orange software Drag the Data Table on the worksheet section to view the selected data, then connect it. 5 Figure 3. Display data table widget in orange software Double-click on the Data Table on the worksheet to see the input data, then a menu like this will appear Figure 4. Display properties data table in orange software Insert the Select Columns widget on the canvas then connect with the widget file Figure 5. Display select columns widget linked to file widgets in orange software 6 To do the classification process on existing data, you can select the Tree widget. And then connect with the Select Column Widget Figure 6. Display of select columns widget linked to the widget tree in orange software Drag the Tree Viewer widget in the worksheet section to see the result obtained, then connect it. Figure 7. Display widget tree linked to widget tree viewer in orange software The next step is to double-click on the Tree Viewer widget to see the existing output. The output is shown in the image below. Figure 8. Display tree viewer properties in orange software 7 To see the evaluation results from the dataset it can be done by selecting the Predictions widget on the evaluate tab. Drag on the canvas and connect with the widget tree and file. Figure 9. Display widget predictions that are connected Double-click on the Predictions widget to see the evaluation values that came out. Figure 10. Display properties predictions in orange software 8 Tree construction begins with the formation of roots (located at the top). Then the data is dividedbased on attributes that are suitable to be used as leaves. Decision rule formation is to make decision
rules from trees that have been formed. The rule can be in the form of if then derived from the decision tree by tracing from root to leaf. Based on the dataset processing using the tree method using Orange software, the accuracy of theDecision Tree classification process is obtained, the value of pecision is 92,4% which means Decision
Tree method is good.
4. Conclusion
The conclusions that can be obtained from the results of the description and discussion can be seen in
Figure 8.
If tear production rate is 1, then type of patient is 3 If tear production rate is 2, then 41,7% type of patient is 2 If tear production rate is 2 and astigmatic is 1, then 83,3% type of patient is 2 If tear production rate is 2 and astigmatic is 2, then 66,7% type of patient is 1 If tear production rate is 2, astigmatic is 1, and age of patient is 3, then 50% type of patient is 2If tear production rate is 2, astigmatic is 1, and age of patient is 1 or 2, then type of patient is 2
If tear production rate is 2, astigmatic is 2 and spectacle prescription is 1, then type of patient is 1 If tear production rate is 2, astigmatic is 2 and spectacle prescription is 2, then 66,7% type of patient is 3For each node and its branch will be given in if, while the value of the value of the leaf will be written
in then. After all the rules are made, the rules can be simplified or combinesAcknowledgements
The author would like to thank all those who helped, assisted and guided in the making of this
research in a journal.References
[1] Hendrian S 2018 Faktor Exacta 11 (3) pp 267-274 [2] Mardi Y 2016 Edik Informatika 2 pp 213-219 [3] Meilina P 2014 Penerapan Data Mining Dengan Metode Klasifikasi Menggunakan DecisionTree dan Regresi Teknologi 7 (1) pp 11-20
[4] Pietersz E, Sumual V and Rares L 2016 Penggunaan Lensa Kontak dan Pengaruhnya Terhadap Dry Eyes Pada Mahasiswa Fakultas Ekonomi Sam Ratulangi e-CliniC 4 (1) [5] Oktanisa I and Supianto A 2017 Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) 5 (5) pp 567-576 [6] Kukasvadiya M and Divecha N 2017 International Journal of Engineering Development andResearch, 5 (2) pp 1836-1840
[7] Ambarsari E, Khotijah S and Sunarmintyastuti L 2019 STRING (Satuan Tulisan Riset danInovasi Teknologi) 4 (1) pp 9-17
[8] Aulia Ishak and Tommy Wijaya 2020 IOP Conf. Ser.: Mater. Sci. Eng. 801 012118 [9] Herviani V and Febriansya A 2016 Riset Akuntansi 8 (2) pp 19-27 [10] Utomo D and Mesran 2020 Jurnal Media Informatika Budidarma 4 (2) 437-444 [11] Irawan N, Wijono and Setyawati O 2017 Perbaikan Missing Value Menggunakan Pendekatan Korelasi Pada Metode K-Nearest Neighbor Jurnal Infotel 9 (3) pp 305-311 [12] Indrayanto G 2018 Natural Product Communications, 13 (12)quotesdbs_dbs12.pdfusesText_18[PDF] orbit altitude of gps satellites
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