Questions tagged [unsupervised-learning]

Finding hidden (statistical) structure in unlabelled data, including clustering and feature extraction for dimensionality reduction.

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8 views

Unsupervised CNN - STN

I would like to implement unsupervised CNN to make affine registration. My input : 64x64x1, binary image of square which are warped, translations and rotations. I use normalize Cross Correlation for ...
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0answers
16 views

Feature selection or Dimension reduction in unsupervised learning

I'm trying to do Embedded clustering using kmeans. This is customer data, so it involves a lot of sentences, so I'm using the universal sentence encoder before clustering. But I should be doing a ...
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0answers
10 views

Optimizing Market Basket Analysis by limiting threshold

I'm creating a suggestion model through MBA. I observed that in my particular model, that if the min_support was placed as 0, the model would take an insanely long ...
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1answer
23 views

Clustering with custom criterion (minimum cluster weight)

Edit: following comment from @anony-mousse, I'm changing the question to search for a general clustering approach that matches this criterion (minimum weight per cluster). I am to use a clustering ...
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1answer
21 views

What machine learning algorithm to use and how to

I have been struggling with this problem for a while now and I finally decided to post a question here to get some help. The problem i'm trying to solve is about predictive maintenance. Specifically, ...
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2answers
20 views

Standardization After PCA for Kmean clustering

I want to apply Kmean for clustering after PCA dimensionality reduction. I have standardized data with StandardScaler before the PCA, then I want to train Kmeans for finding clusters. However, the ...
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1answer
23 views

Methodology for driving score(behavior)

I am an intern at mobility data company and a Master's candidate in Statistics. I am researching about driving score which is based on a driver's driving habit. We have trip data which contains the ...
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23 views
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20 views

Unsupervised learning/ clustering for data with multiple categorical variables

Dataset: I have been trying unsupervised clustering algorithms (K-modes & SOM) to cluster the students based on their grades in 3 exams. Should I one-hot encode the data (even though grades are ...
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0answers
36 views

Map predictions to real text

I have read the paper "Learning to Read by Spelling" by Gutpa et al. They present a method for visual text recognition without using any paired supervisory data. In chapter 4 they describe how to ...
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3answers
24 views

K-modes clustering: Estimating which features were most impactful on clustering?

I have entirely categorical data (survey results from users), so I've used k-modes clustering to better understand my users. I'm not an expert at clustering methods at all. Is there a way to known ...
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2answers
30 views

How can Clustering (Unsupervised Learning) be used to improve the accuracy of Linear Regression model (Supervised Learning)

I came through this questions and I failed to find the right answer for it. How can Clustering (Unsupervised Learning) be used to improve the accuracy of Linear Regression model (Supervised Learning)?...
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0answers
13 views

Can I save an Unsupervised Deep Learning model?

I'm trying to solve some unsupervised problem with Deep Learning but I'm working with a Java Desktop application. I want to use Deeplearning4J for this task. However, I still want to code in python. ...
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0answers
11 views

semi supervised learning using transfer learning and shared memory

I am reading a paper here and I am not sure I am understanding something. They claim to have 83% unsupervised on CIFAR 10, but they used something that is semi supervised. At the very least, they used ...
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0answers
15 views

How does t-sne scale work? Is it possible to compare scales of two t-sne plots?

I have used t-sne for two convolutional layers of binary classifier (VGG16 like CNN). I want to compare them, but I am not sure if that is okay as the scales of these two outputs from t-SNE plots can ...
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2answers
26 views

What kind of learning in this training situation when anomaly detection? Supervised learning,semi-supervised learning or unsupervised learning?

I am doing anomaly detection recently, one of the methods is using AEs model to learn the pattern of normal samples. Determine it as an abnormal sample if it doesn’t match the pattern of normal ...
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1answer
45 views

Clustering vs Non Clustering problems?

I'm just getting started with Andrew Ng's Machine Learning wherein he explained the example of the cocktail party problem vs the gene clustering problem in order to explain the difference between ...
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0answers
19 views

Unsupervised clustering improved with supervised classification accuracy

I have a set of labeled samples each containing up to 300 different objects. For every object I have a set of features describing the object. For example, Sample with label '1': 50 objects of type ...
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0answers
23 views

Which unsupervised learning algorithm can be used for peaks detection?

So, I have a dataset which has around 1388 unique products and I have to do unsupervised learning on them in order to find anomalies (high/low peaks). The data below just represents one product. The <...
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0answers
28 views

sklearn & Meanshift for NLP only returns 1 cluster

I am using sklearn.clustering to work with some text data and the MeanShift algorithm. I have: Done all standard NLP data prep like lemmatizing, removing stop ...
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3answers
129 views

Looking for a classification (?) algorithm for linearly separable but unlabeled data points

I have a dataset that is linearly separable with two lines - something like that: Now I'am looking for the right kind of algorithm to do what I guess a SVM would do with labeled data - find the ...
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1answer
42 views

Using an unsupervised Isolation Forest, how does one identify the optimal number of outliers from the anomaly scores?

I am using an unsupervised isolation forest algorithm and computing anomaly scores to detect outliers from a 2 dimensional toy dataset. From a scatter plot, I am able to detect/visualize the data ...
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1answer
19 views

How to cluster text-based software requirements

I'm beginner in deep learning and I'd like to cluster text-based software requirements by themes (words similarities/frequency of words) using neural networks. Is there any example/tutorial/github ...
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1answer
28 views

Clustering for variables with large amount of categories

I have a dataset which, has variables with a lot of categories (some more than 1000). Since, large amount of categories effect the accuracy of the model. I saw some literature stating that if you do ...
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1answer
70 views

Kmeans clustering with multiple columns containing strings

I have the following dataset: https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset What I want to find is clusters based on imdb score per genre per country. I have created a pandas data frame ...
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1answer
152 views

Simple example of Parzen window (kernel density estimation)

I am confused about the Parzen Window question. Suppose we have two training data points located at 0.5 and 0.7, and we use 0.3 as its rectangle window width. How do we estimate its probability ...
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1answer
90 views

Unsupervised learning for anomaly detection

I've started working on an anomaly detection in Python. My dataset is a time series one. The data is being collected by some sensors which record and collect data on semiconductor making machines. ...
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1answer
106 views

How to compare two unsupervised anomaly detection algorithms on the same data-set?

I want to solve an anomaly detection problem on an unlabeled data-set. The only information about this problem is that the anomalies population is lower than 0.1%. It should be notice that the size of ...
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1answer
309 views

Clustering based on distance between points [closed]

I am trying to cluster geographical locations in such a way that all the locations inside each cluster are at max within 25 miles of each other. For this, I am using Agglomerative clustering. I am ...
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1answer
41 views

What does Make Density Based Clusterer in Weka do?

In Weka, there is a clustering algorithm with the name as Make Density Based Clusterer. When going through its properties, it takes a clusterer as base clusterer(I took it as K-means with k=3). It ...
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3answers
113 views

Unsupervised Anomaly Detection on system metrics like memory, cpu, io, net, etc

In all the examples that I can see online, people have used a labelled dataset. I however am stuck trying to construct a model to perform anomaly detection on unlabelled dataset (unsupervised anomaly ...
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1answer
218 views

Anomaly detection on multidimensional time series

I have relatively little knowledge of unsupervised machine learning. I'm working on a project that aims to find anomalies in a set of n data, measured every ...
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0answers
230 views

How to set the Reconstruction error threshold for anomaly detection using autoencoders?

Hi I am doing anomaly detection using auto encoders.I have trained the model using 'Non Anomalous' values.Now when I give anomalous points as test data. What should be the Reconstruction error ...
2
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1answer
42 views

Is splitting the data set into train and validation applicable in unsupervised learning?

I am having a tough time implementing all the steps of setting up support vector machine (SVM) for unsupervised learning. My data set is labelled but for educational purposes I am learning ...
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1answer
245 views

Gaussian Mixture Models as a classifier?

I'm learning the GMM clustering algorithm. I don't understand how it can used as a classifier. Here are my thought: 1) GMM is an unsupervised ML algorithm. At least that's how ...
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1answer
18 views

How can I perform clustering on a list of words and ratings as columns?

I want to perform clustering to give words meaning like good, neutral and bad. My dataset is in the format : ...
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2answers
57 views

Grouping already clustered data (with a pre-defined x and y)

I have an already clustered data set (I wanna keep my x and y), where there's clearly a small group of elements in the middle that don't follow the expected patterns. I can select them manually, but ...
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1answer
37 views

Unsupervised Learning and Training Data

As far as I know, we need to use training data to find out the relation between the features, also known as input values, and labels, that are output values, in supervised learning. After that, by ...
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1answer
20 views

Supervised or unsupervised learning for predicting energy consumption for new buildings

I’m working on an model for auto dimensioning district heating pipes for new district heating areas (new customers). I have energy consumption data on hourly basis and describe data about these ...
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0answers
8 views

What methods exist for recommendation based on implicit information?

Assume we have a dataset of which products a user is using on a monthly basis. Let's further assume that the number of users is $n$ and the number of products is $p$ and that we are in the $p\ll n$ ...
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1answer
31 views

Gibbs sampling (For inference) vs EM

I'm familiar with the Expectation-Maximixation algorithm and, until now, I thought it was the only way to maximize the likelihood of the observed data, assuming a Gaussian mixture model. In the last ...
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0answers
20 views

I need help interpreting this PCA plot

I have a dataset of 116 observations and 10 numeric variables. The dataset contains information about healthy patients and patients attained with breast cancer. I did a PCA plot showing the cluster of ...
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0answers
72 views

PCA for unsupervised feature selection [closed]

If I understood correctly, "using results of PCA to select features" (as recommended in this answer) implies visually analysing bi-plots of first two principal components - i.e. the angle between a ...
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1answer
31 views

what is a good performance measure for comparing different neural network architectures in unsupervised clustering task?

What is a good measure to use when trying to decide between picking unsupervised clustering NN architectures? There seems to some ideas here, but i am trying to find out feedback/suggestions from ...
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1answer
46 views

Kmeans cluster validation when I have labeled test data

I'm trying to implement the unsupervised k-means algorithm for sentiment analysis of imdb movie dataset created by stanford. The steps that I followed is : 1) Load the comments 2) Apply tokenization ...
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3answers
122 views

How to create clusters based on sentence similarity?

I have data which looks like following. Data is a group of sentences which are similar, but have few unique words in between like TABLEA, TABLEB etc. ...
2
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3answers
67 views

ML algorithm for Music Features

I am a newbie in machine learning topic and I need to create model from music data. It contains features of the songs but it is not labeled. How can I create a model from that ? Do I need to use ...
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1answer
47 views

Unsupervised learning from images [closed]

I want to design a model that can detect the different feature in the images, let's consider we have ~100000 images of cows. when I give this images to the model it has to identify different parts of ...
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1answer
55 views

Clustering Customers on transactional behavior

Objective: Segment the accounts on their transactional behavior and find the accounts which are more likely to subscribe for loans. Dataset: 1) Account_Number 2-91) Transaction amount ...
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0answers
117 views

Training detector without bounding box data

From what I can see most object detection NNs (Fast(er) R-CNN, YOLO etc) are trained on data including bounding boxes indicating where in the picture the objects are localized. Is there any model ...