Orange: Distances - Revision history

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Onnowpurbo: Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/distances.html Computes distances between rows/columns in a dataset. Inputs Data: input datas..."

2020-01-16T23:55:52Z

Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/distances.html Computes distances between rows/columns in a dataset. Inputs Data: input datas..."

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Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/distances.html

Computes distances between rows/columns in a dataset.

Inputs

Data: input dataset

Outputs

Distances: distance matrix

The Distances widget computes distances between rows or columns in a dataset. By default, the data will be normalized to ensure equal treatment of individual features. Normalization is always done column-wise.

Sparse data can only be used with Euclidean, Manhattan and Cosine metric.

The resulting distance matrix can be fed further to Hierarchical Clustering for uncovering groups in the data, to Distance Map or Distance Matrix for visualizing the distances (Distance Matrix can be quite slow for larger data sets), to MDS for mapping the data instances using the distance matrix and finally, saved with Save Distance Matrix. Distance file can be loaded with Distance File.

Distances work well with Orange add-ons, too. The distance matrix can be fed to Network from Distances (Network add-on) to convert the matrix into a graph and to Duplicate Detection (Text add-on) to find duplicate documents in the corpus.

../../_images/Distances-stamped.png

Choose whether to measure distances between rows or columns.

Choose the Distance Metric:

Euclidean (“straight line”, distance between two points)

Manhattan (the sum of absolute differences for all attributes)

Cosine (the cosine of the angle between two vectors of an inner product space)

Jaccard (the size of the intersection divided by the size of the union of the sample sets)

Spearman(linear correlation between the rank of the values, remapped as a distance in a [0, 1] interval)

Spearman absolute(linear correlation between the rank of the absolute values, remapped as a distance in a [0, 1] interval)

Pearson (linear correlation between the values, remapped as a distance in a [0, 1] interval)

Pearson absolute (linear correlation between the absolute values, remapped as a distance in a [0, 1] interval)

Hamming (the number of features at which the corresponding values are different)

Bhattacharyya distance (Similarity between two probability distributions, not a real distance as it doesn’t obey triangle inequality.)

Normalize the features. Normalization is always done column-wise. Values are zero centered and scaled. In case of missing values, the widget automatically imputes the average value of the row or the column. The widget works for both numeric and categorical data. In case of categorical data, the distance is 0 if the two values are the same (‘green’ and ‘green’) and 1 if they are not (‘green’ and ‘blue’).

Tick Apply Automatically to automatically commit changes to other widgets. Alternatively, press ‘Apply’.

Examples

The first example shows a typical use of the Distances widget. We are using the iris.tab data from the File widget. We compute distances between data instances (rows) and pass the result to the Hierarchical Clustering. This is a simple workflow to find groups of data instances.

../../_images/Distances-Example1-rows.png

Alternatively, we can compute distance between columns and find how similar our features are.

../../_images/Distances-Example1-columns.png

The second example shows how to visualize the resulting distance matrix. A nice way to observe data similarity is in a Distance Map or in MDS.

../../_images/Distances-Example2.png

==Referensi==

* https://docs.biolab.si//3/visual-programming/widgets/unsupervised/distances.html

==Pranala Menarik==

* [[Orange]]

@@ Line 39: / Line 39: @@
 ==Contoh==
-Contoh pertama menunjukkan penggunaan widget Distances. Kita menggunakan data iris.tab dari widget File. Kita menghitung jarak antara instance data (baris) dan meneruskan hasilnya ke Hierarchical Clustering. Ini adalah alur kerja sederhana untuk menemukan grup di instance data.
+Contoh pertama menunjukkan penggunaan widget Distances. Kita menggunakan data iris.tab dari widget File. Kita menghitung jarak antara instance data (baris) dan meneruskan hasilnya ke Hierarchical Clustering. Berikut adalah Workflow sederhana untuk menemukan grup / cluster dalam instance data.
 [[File:Distances-Example1-rows.png|center|200px|thumb]]

@@ Line 18: / Line 18: @@
 Matrix distance oleh Widget Distances yang dihasilkan dapat diumpankan lebih lanjut ke Widget Hierarchical Clustering untuk mengungkap group dalam data, ke Widget Distance Map atau Widget Distance Matrix untuk memvisualisasikan jarak (Widget Distance Matrix bisa sangat lambat untuk dataset yang besar), ke Widget MDS untuk memetakan contoh data menggunakan matrix distance dan akhirnya, disimpan dengan Widget Save Distance Matrix. Distance File dapat di load dengan Widget Distance File.
-Distances juga bekerja dengan baik dengan add-on Orange. Distance Matrix dapat diumpankan ke Network from Distances (Network add-on) untuk mengubah matrix menjadi graph dan ke Duplicate Detection (Text add-on) untuk menemukan duplikasi dokumen dalam corpus.
+Widget Distances juga bekerja dengan baik dengan add-on Orange lainnya. Widget Distance Matrix dapat diumpankan ke Widget Network from Distances (Network add-on) untuk mengubah matrix menjadi graph dan ke Widget Duplicate Detection (Text add-on) untuk menemukan duplikasi dokumen dalam corpus.
 [[File:Distances-stamped.png|center|200px|thumb]]

@@ Line 16: / Line 16: @@
 Sparse data hanya bisa digunakan dengan Euclidean, Manhattan dan Cosine metric.
-Matrix distance yang dihasilkan dapat diumpankan lebih lanjut ke Hierarchical Clustering untuk mengungkap group dalam data, ke Distance Map atau Distance Matrix untuk memvisualisasikan jarak (Distance Matrix bisa sangat lambat untuk dataset  yang besar), ke MDS untuk memetakan contoh data menggunakan matrix distance dan akhirnya, disimpan dengan Save Distance Matrix. Distance File dapat di load dengan Distance File.
+Matrix distance oleh Widget Distances yang dihasilkan dapat diumpankan lebih lanjut ke Widget Hierarchical Clustering untuk mengungkap group dalam data, ke Widget Distance Map atau Widget Distance Matrix untuk memvisualisasikan jarak (Widget Distance Matrix bisa sangat lambat untuk dataset yang besar), ke Widget MDS untuk memetakan contoh data menggunakan matrix distance dan akhirnya, disimpan dengan Widget Save Distance Matrix. Distance File dapat di load dengan Widget Distance File.
 Distances juga bekerja dengan baik dengan add-on Orange. Distance Matrix dapat diumpankan ke Network from Distances (Network add-on) untuk mengubah matrix menjadi graph dan ke Duplicate Detection (Text add-on) untuk menemukan duplikasi dokumen dalam corpus.

@@ Line 39: / Line 39: @@
 ==Contoh==
-The first example shows a typical use of the Distances widget. We are using the iris.tab data from the File widget. We compute distances between data instances (rows) and pass the result to the Hierarchical Clustering. This is a simple workflow to find groups of data instances.
+Contoh pertama menunjukkan penggunaan widget Distances. Kita menggunakan data iris.tab dari widget File. Kita menghitung jarak antara instance data (baris) dan meneruskan hasilnya ke Hierarchical Clustering. Ini adalah alur kerja sederhana untuk menemukan grup di instance data.
 [[File:Distances-Example1-rows.png|center|200px|thumb]]
-Alternatively, we can compute distance between columns and find how similar our features are.
+Atau, kita dapat menghitung jarak antar kolom dan menemukan betapa miripnya feature-feature yang ada.
 [[File:Distances-Example1-columns.png|center|200px|thumb]]
-The second example shows how to visualize the resulting distance matrix. A nice way to observe data similarity is in a Distance Map or in MDS.
+Contoh kedua menunjukkan bagaimana memvisualisasikan matrix distance yang dihasilkan. Cara yang bagus untuk mengamati kesamaan data adalah dalam Distance Map atau dalam MDS.
 [[File:Distances-Example2.png|center|200px|thumb]]
 ==Referensi==

@@ Line 4: / Line 4: @@
 Computes distances between rows/columns in a dataset.
-Inputs
+==Input==
      Data: input dataset
-Outputs
+==Output==
      Distances: distance matrix
@@ Line 22: / Line 22: @@
 [[File:Distances-stamped.png|center|200px|thumb]]
+* Choose whether to measure distances between rows or columns.
-    Choose whether to measure distances between rows or columns.
+* Choose the Distance Metric:
-    Choose the Distance Metric:
+** Euclidean (“straight line”, distance between two points)
-        Euclidean (“straight line”, distance between two points)
+** Manhattan (the sum of absolute differences for all attributes)
-        Manhattan (the sum of absolute differences for all attributes)
+** Cosine (the cosine of the angle between two vectors of an inner product space)
-        Cosine (the cosine of the angle between two vectors of an inner product space)
+** Jaccard (the size of the intersection divided by the size of the union of the sample sets)
-        Jaccard (the size of the intersection divided by the size of the union of the sample sets)
+** Spearman(linear correlation between the rank of the values, remapped as a distance in a [0, 1] interval)
-        Spearman(linear correlation between the rank of the values, remapped as a distance in a [0, 1] interval)
+** Spearman absolute(linear correlation between the rank of the absolute values, remapped as a distance in a [0, 1] interval)
-        Spearman absolute(linear correlation between the rank of the absolute values, remapped as a distance in a [0, 1] interval)
+** Pearson (linear correlation between the values, remapped as a distance in a [0, 1] interval)
-        Pearson (linear correlation between the values, remapped as a distance in a [0, 1] interval)
+** Pearson absolute (linear correlation between the absolute values, remapped as a distance in a [0, 1] interval)
-        Pearson absolute (linear correlation between the absolute values, remapped as a distance in a [0, 1] interval)
+** Hamming (the number of features at which the corresponding values are different)
-        Hamming (the number of features at which the corresponding values are different)
+** Bhattacharyya distance (Similarity between two probability distributions, not a real distance as it doesn’t obey triangle inequality.)
-        Bhattacharyya distance (Similarity between two probability distributions, not a real distance as it doesn’t obey triangle inequality.)
+* Normalize the features. Normalization is always done column-wise. Values are zero centered and scaled. In case of missing values, the widget automatically imputes the average value of the row or the column. The widget works for both numeric and categorical data. In case of categorical data, the distance is 0 if the two values are the same (‘green’ and ‘green’) and 1 if they are not (‘green’ and ‘blue’).
-    Normalize the features. Normalization is always done column-wise. Values are zero centered and scaled. In case of missing values, the widget automatically imputes the average value of the row or the column. The widget works for both numeric and categorical data. In case of categorical data, the distance is 0 if the two values are the same (‘green’ and ‘green’) and 1 if they are not (‘green’ and ‘blue’).
+* ick Apply Automatically to automatically commit changes to other widgets. Alternatively, press ‘Apply’.
 ==Contoh==