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Questions tagged [unsupervised-learning]

Unsupervised learning refers to machine learning contexts in which there is no prior 'training' period in which the learning agent is trained on objects of known type. As such, supervised learning includes such disciplines as mathematical clustering, whereby data is segmented into clusters based on the minimisation or maximisation of mathematical properties and not on an attempt to classify by understanding the right context.

Unsupervised learning (or clustering) refers to machine learning algorithms in which there is no 'label' available for the training data and the model tries to learn the underlying manifold. As such, unsupervised learning includes such disciplines as mathematical clustering, whereby data is segmented into clusters based on the minimization or maximization of mathematical properties and not on an attempt to classify by understanding the right context.

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Does Imagenet contain unlabeled data?

Does Imagenet contain unlabeled examples like STL-10 dataset? because I see that the dataset is used for unsupervised learning but it seems to me it has only labeled examples.
seni
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number of neurons in the output layer of a competitive learning ANN

How to determine the number of neurons in the output layer of a competitive learning ANN? Does the value depend on how many classes that we need to predict?
Lakshan Weerasinghe
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What are the performance metrics for Clustering Algorithms?

I'm working on Kmeans clustering but unlike supervised learning I cannot figure the performance metrics for clustering algorithms. How to perform the accuracy after training the data?
Komali
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If a DBSCAN algorithm is working correctly, is it possible to result in a cluster with less than minPoints members?

I am new to using the DBSCAN algorithm. Quick summary; it has two parameters: epsilon - to specify the acceptable "distance" between two points, under which they can be considered close enough to cluster. minPoints - to specify the minimum number…
benjamin
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Learning algorithm to implement XOR gate

I know we can't use perceptron learning algorithm to implement XOR gate because it is a lineraly inseparable problem. So my question is which learning algorithm and which neural network can we use to implement XOR gate? I tried using Delta rule, but…
Vipul Tyagi
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Getting back the string values from previously label encoded values in python?

I wanted to predict some string values using unsupervised learning. I converted a categorical column into numerical by using label encoding and predicted the values. Now I want to know the string values of the predicted numerical values(label…
krish
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TypeError: __init__() got an unexpected keyword argument 'madataset_iter'

My question is about K-means clustering algorithm, I keep getting the error TypeError: __init__() got an unexpected keyword argument 'madataset_iter' {it was sorted out after correcting the typo error} when I try to visualize the clusters after…
Babatunde Victor
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Can Tensor Flow be used for unsupervised learning for solving nlp classification?

Is it possible to use Tensorflow for text classification problems for unlabeled data?
ricky
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Can different summary metrics of a single feature be used as a features for k-means clustering?

I have a scenario where i wanted to understand the customers behavior pattern and group them into different segments/clusters for an e-commerce platform. I choose to un-supervised machine learning algorithm: k-means clustering to accomplish this…
Rajiv2806
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Clustering in R levenshtein distance

I am trying to use kmeans clustering using the levenshtein distance. I am having hard time in interpeting the results. # courtesy: code is borrowed from the other thread listed below with some additions of k-means clustering set.seed(1) …
user3570187
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Changing the results of Kmeans algorithm

I'm using KBinsDiscretizer to cluster my data to four categories using kmeans algorithm as follow. The goal is to have 4 clusters based on the value of avg_error. The code works properly and returns 4 clusters as: 0: very low error rate, 1: low…
Birish
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Selecting the K value for Kmeans clustering

I am going to build a K-means clustering model for outlier detection. For that, I need to identify the best number of clusters needs to be selected. For now, I have tried to do this using Elbow Method. I plotted the sum of squared error vs. the…
Ayesh Weerasinghe
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How to identify and separate clusters using K Means in python?

I'm trying to find clusters in a data set using K-means method. I got the number of clusters from the elbow method, but don't know how to identify and separate these clusters for further analysis on each cluster like applying linear regression on…
Bhavishya
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Find which points belong to a cluster in DBSCAN in python

Hi Guys I have fitted a DBSCAN model on a set of points (4953 points). Now I need to find the points which belong to different clusters i.e which all input values belong to which all clusters.I have total of 10 clusters.How can I find this out? db =…
Fasty
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Deep learning classification with no labels

I must participate in a research project regarding a deep learning application for classification. I have a huge dataset containing over 35000 features - these are good values, taken from laboratory. The idea is that I should create a classifier…
Vanhaeren Thomas
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