Orange: Principal Component Analysis - Revision history

Unknown user at 01:29, 14 February 2025

2025-02-14T01:29:36Z

← Older revision		Revision as of 01:29, 14 February 2025
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	[[File:Pca.png\|center\|200px\|thumb]]		[[File:Pca.png\|center\|200px\|thumb]]



			The workflow in '''Orange Data Mining''' shown in the picture follows a '''Principal Component Analysis (PCA)''' approach for dimensionality reduction and visualization. Here’s the step-by-step breakdown:

			'''1. File (Data Loading)'''
			* The '''File''' widget loads the dataset named '''"brown-selected"''', which is from '''molecular biology'''.
			* The dataset contains '''79 features''', '''186 instances''', and '''3 classes'''.

			'''2. PCA (Dimensionality Reduction)'''
			* The '''PCA (Principal Component Analysis)''' widget is used to reduce the dataset's dimensionality.
			* Users can '''open the scree diagram''' to '''interactively select the number of principal components'''.
			* PCA helps in identifying '''the most important features''' that explain the variance in the dataset.

			'''3. Data Table (Exploring Transformed Data)'''
			* The '''Data Table''' widget displays the '''transformed dataset''' after applying PCA.
			* It allows users to inspect how the original data is represented in the new principal components.

			'''4. Scatter Plot (Visualizing Principal Components)'''
			* The '''Scatter Plot''' widget is used to '''plot the two best principal components'''.
			* This helps in '''checking whether the classes in the input dataset are well separated'''.
			* If the classes are well-separated in the plot, it indicates that PCA has successfully captured meaningful variations in the data.

			==Summary==

			This '''Orange Data Mining''' workflow loads a '''molecular biology dataset''', applies '''PCA for dimensionality reduction''', and then visualizes the '''principal components''' using a '''scatter plot''' to check if different classes are well-separated. The '''Data Table''' is used for further exploration of the transformed data. This approach is useful for '''feature selection''', '''data visualization''', and '''understanding class separability''' in high-dimensional datasets.





	==Source==		==Source==

Unknown user: /* Source */

2022-03-15T02:05:50Z

Source

← Older revision		Revision as of 02:05, 15 March 2022
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	==Source==		==Source==

	* https://~~service~~.~~biolab.si~~/~~download~~/~~workflow?name=305-pca.ows&domain=orange~~		* https://orangedatamining.com/workflows/PCA/

	==Referensi==		==Referensi==

Onnowpurbo: /* Pranala Menarik */

2020-02-22T20:11:19Z

Pranala Menarik

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	* [[Orange]]		* [[Orange]]
			* [[Principal Component Analysis (PCA)]]

Onnowpurbo at 20:11, 22 February 2020

2020-02-22T20:11:04Z

← Older revision		Revision as of 20:11, 22 February 2020
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	PCA mengubah data menjadi dataset dengan variabel tidak berkorelasi, juga disebut principal components. Widget PCA menampilkan grafik (diagram scree) yang menunjukkan tingkat perbedaan yang dijelaskan oleh best principal components dan memungkinkan untuk secara interaktif mengatur jumlah komponen yang akan dimasukkan dalam dataset keluaran. Dalam workflow ini, kita bisa mengamati transformasi di Tabel Data dan di Scatter Plot.		[[Principal Component Analysis (PCA) \| PCA]] mengubah data menjadi dataset dengan variabel tidak berkorelasi, juga disebut principal components. Widget PCA menampilkan grafik (diagram scree) yang menunjukkan tingkat perbedaan yang dijelaskan oleh best principal components dan memungkinkan untuk secara interaktif mengatur jumlah komponen yang akan dimasukkan dalam dataset keluaran. Dalam workflow ini, kita bisa mengamati transformasi di Tabel Data dan di Scatter Plot.

Onnowpurbo at 00:09, 28 January 2020

2020-01-28T00:09:27Z

← Older revision		Revision as of 00:09, 28 January 2020
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	==Referensi==		==Referensi==

			* YOUTUBE: https://www.youtube.com/watch?v=lX8Eim1a0s4
	* https://orange.biolab.si/workflows/		* https://orange.biolab.si/workflows/

Onnowpurbo: Created page with "Sumber: https://orange.biolab.si/workflows/ PCA mengubah data menjadi dataset dengan variabel tidak berkorelasi, juga disebut principal components. Widget PCA menampilkan g..."

2020-01-08T01:18:35Z

Created page with "Sumber: https://orange.biolab.si/workflows/ PCA mengubah data menjadi dataset dengan variabel tidak berkorelasi, juga disebut principal components. Widget PCA menampilkan g..."

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Sumber: https://orange.biolab.si/workflows/

PCA mengubah data menjadi dataset dengan variabel tidak berkorelasi, juga disebut principal components. Widget PCA menampilkan grafik (diagram scree) yang menunjukkan tingkat perbedaan yang dijelaskan oleh best principal components dan memungkinkan untuk secara interaktif mengatur jumlah komponen yang akan dimasukkan dalam dataset keluaran. Dalam workflow ini, kita bisa mengamati transformasi di Tabel Data dan di Scatter Plot.

[[File:Pca.png|center|200px|thumb]]

==Source==

* https://service.biolab.si/download/workflow?name=305-pca.ows&domain=orange

==Referensi==

* https://orange.biolab.si/workflows/

==Pranala Menarik==

* [[Orange]]