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1. Top results related to unsupervised learning

   • Unsupervised learning clustering 1D array

     Top Answer

     Answered Jul 25, 2018 · 9 votes

     Try MeanShift

     . From the sklean user guide of MeanShift:

     The algorithm automatically sets the number of clusters, ...

     Modified demo code:

     import numpy as npfrom sklearn.cluster import MeanShift, estimate_bandwidth# ############################################################################## Generate sample dataX = [1,2,4,7,9,5,4,7,9,56,57,54,60,200,297,275,243]X = np.reshape(X, (-1, 1))# ############################################################################## Compute clustering with MeanShift# The following bandwidth can be automatically detected using# bandwidth = estimate_bandwidth(X, quantile=0.2, n_samples=100)ms = MeanShift(bandwidth=None, bin_seeding=True)ms.fit(X)labels = ms.labels_cluster_centers = ms.cluster_centers_labels_unique = np.unique(labels)n_clusters_ = len(labels_unique)print("number of estimated clusters : %d" % n_clusters_)print(labels)

     Output:

     number of estimated clusters : 2[0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1]

     Note that MeanShift is not scalable with the number of samples. The recommended upper limit is 10,000.

     BTW, as rahlf23 already mentioned, K-mean is an unsupervised learning algorithm. The fact that you have to specify the number of clusters does not mean it is supervised.

     See also:

     • Overview of clustering methods
     • Choosing the right estimator

     Check all answers on Stack Overflow

     1/5

   • Unsupervised Learning

     Unsupervised learning is a branch of machine learning that deals with unlabeled data. Unlike supervised learning, where the data is labeled with a specific category or outcome, unsupervised learning algorithms are tasked with finding patterns and relationships within the data without any prior knowledge of the data’s meaning. This makes unsupervised learning a powerful tool for exploratory data analysis, where the goal is to understand the underlying structure of the data.

     Unsupervised Learning

     In artificial intelligence, machine learning that takes place in the absence of human supervision is known as unsupervised machine learning. Unsupervised machine learning models, in contrast to supervised learning, are given unlabeled data and allow discover patterns and insights on their own—without explicit direction or instruction.

     Unsupervised machine learning analyzes and clusters unlabeled datasets using machine learning algorithms. These algorithms find hidden patterns and data without any human intervention, i.e., we don’t give output to our model. The training model has only input parameter values and discovers the groups or patterns on its own.

     Unsupervised Learning

     How does unsupervised learning work?

     Unsupervised learning works by analyzing unlabeled data to identify patterns and relationships. The...

     Check all answers on Geeksforgeeks

     2/5

   • Unsupervised Learning

     Unsupervised learning is a branch of machine learning that deals with unlabeled data. Unlike supervised learning, where the data is labeled with a specific category or outcome, unsupervised learning algorithms are tasked with finding patterns and relationships within the data without any prior knowledge of the data’s meaning. This makes unsupervised learning a powerful tool for exploratory data analysis, where the goal is to understand the underlying structure of the data.

     Unsupervised Learning

     In artificial intelligence, machine learning that takes place in the absence of human supervision is known as unsupervised machine learning. Unsupervised machine learning models, in contrast to supervised learning, are given unlabeled data and allow discover patterns and insights on their own—without explicit direction or instruction.

     Unsupervised machine learning analyzes and clusters unlabeled datasets using machine learning algorithms. These algorithms find hidden patterns and data without any human intervention, i.e., we don’t give output to our model. The training model has only input parameter values and discovers the groups or patterns on its own.

     Unsupervised Learning

     How does unsupervised learning work?

     Unsupervised learning works by analyzing unlabeled data to identify patterns and relationships. The...

     Check all answers on Geeksforgeeks

     3/5

   • Unsupervised machine learning with scikit learn

     Top Answer

     Answered Nov 13, 2018 · 0 votes

     May be you want to see the spread of data, with respect to first two features. Then you have to slice it from the initial dataset instead from Series (as @Alexandre metioned)

     data = arff.loadarff("./Data/Arrhythmia/Arrhythmia_withoutdupl_02_v01.arff")df = pd.DataFrame(data[0])df = df.drop(['outlier',"id"],axis=1)plt.scatter(df.iloc[:,0],df.iloc[:,1])plt.show()

     Check all answers on Stack Overflow

     4/5

   • Also try

     supervised and unsupervised learning	perbedaan supervised dan unsupervised learning
     unsupervised learning in machine learning	unsupervised learning techniques
     unsupervised learning example	unsupervised learning adalah
     unsupervised learning algorithms	unsupervised learning definition

     5/5

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2. en.wikipedia.org › wiki › Unsupervised_learningUnsupervised learning - Wikipedia

   en.wikipedia.org › wiki › Unsupervised_learning

   • Cached

   Unsupervised learning is a method in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data. Within such an approach, a machine learning model tries to find any similarities, differences, patterns, and structure in data by itself.

3. www.ibm.com › topics › unsupervised-learningWhat Is Unsupervised Learning? | IBM

   www.ibm.com › topics › unsupervised-learning

   • Cached

   Unsupervised learning, also known as unsupervised machine learning, uses machine learning (ML) algorithms to analyze and cluster unlabeled data sets. These algorithms discover hidden patterns or data groupings without the need for human intervention.

4. www.geeksforgeeks.org › ml-types-learning-part-2Unsupervised Learning - GeeksforGeeks

   www.geeksforgeeks.org › ml-types-learning-part-2

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   Dec 4, 2023 · Supervised and unsupervised learning are two fundamental approaches to machine learning that differ in their training data and learning objectives. Supervised learning involves training a machine learning model on a labeled dataset, where each data point has a corresponding label or output value.

5. www.datacamp.com › blog › introduction-toIntroduction to Unsupervised Learning - DataCamp

   www.datacamp.com › blog › introduction-to

   • Cached

   Unsupervised learning refers to a class of problems in machine learning where a model is used to characterize or extract relationships in data. In contrast to supervised learning, unsupervised learning algorithms discover the underlying structure of a dataset using only input features.

6. cloud.google.com › what-is-unsupervised-learningWhat is unsupervised learning? | Google Cloud

   cloud.google.com › what-is-unsupervised-learning

   Unsupervised learning in artificial intelligence is a type of machine learning that learns from data without human supervision. Unlike supervised learning, unsupervised machine learning...

7. www.geeksforgeeks.org › supervised-unsupervisedSupervised and Unsupervised learning - GeeksforGeeks

   www.geeksforgeeks.org › supervised-unsupervised

   • Cached

   Mar 13, 2024 · Supervised and unsupervised learning are two fundamental approaches to machine learning that differ in their training data and learning objectives. Supervised learning involves training a machine learning model on a labeled dataset, where each data point has a corresponding label or output value.

8. scikit-learn.org › stable › unsupervised_learning2. Unsupervised learning — scikit-learn 1.5.1 documentation

   scikit-learn.org › stable › unsupervised_learning

   • Cached

   1. Supervised learning. 1.1. Linear Models; 1.2. Linear and Quadratic Discriminant Analysis; 1.3. Kernel ridge regression; 1.4. Support Vector Machines; 1.5. Stochastic Gradient Descent; 1.6. Nearest Neighbors; 1.7. Gaussian Processes; 1.8. Cross decomposition; 1.9. Naive Bayes; 1.10. Decision Trees; 1.11. Ensembles: Gradient boosting, random ...