Questions tagged [unsupervised-learning]

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

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24 views

Metrics for unsupervised doc2vec model

I have just built a simple doc2vec model using the gensim library, pretty much followed the tutorial located here. The methods provided for checking the quality of the model are very manual and ...
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1answer
26 views

What machine learning algorithm should I use for specific user configuration?

I have a data-set that contains thousands of employee data, including their role, department (Applications Developer, IT Support, Network Management etc.), and using one-hot encoding all of the ...
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1answer
22 views

Image clustering with deep learning

I want to cluster image, since varibility intra and inter class of images is huge I think reducing dimensions with a convolutional autoencodeur can be a good tools. Then I apply clustering on the ...
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1answer
32 views

Is there any method to determine which clustering algorithm to use on a particular dataset?

I'm having a hard time getting kmeans to cluster data effectively. It fails to segment data well even for a simple attribute with 5 categories. I'm aware of DBSCAN, Hierarchical Clustering and GMM. ...
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1answer
28 views

Clustering (unsupervised learning) for uneven classes

I am looking for an unsupervised method that can see also the points that start to look different from the majority. Which clustering techniques (I use python) can be used for such data sets? I have ...
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2answers
45 views

Applying and Visualizing k means clustering on a data set that has 9 features

I had a data set of images that I have extracted 9 numerical features that I want to apply k means clustering or hierarchical clustering to. I'm just not sure how to go about it. The tutorials I have ...
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44 views

Anomaly detection using k-means clustering in Python

I'm working on an anomaly detection task in Python. Datasets regard a collection of time series coming from a sensor, so data are timestamps and the relative values. In order to find anomalies, I'm ...
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0answers
19 views

Not sure what to do after feature extraction for a clustering problem I'm working on

I am very new to the machine learning scene, and I'm trying to do clustering(kmeans or hierarchical clustering) on a data set of about 31k black and white images of size 200x200 (Each image is ...
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10 views

Want to understand how Local outlier works

I am trying to understand how the local outlier factor algorithm works. I have not been able to find a decent and easy explanation of the same. I came across the post: Local Outlier Factor For ...
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0answers
15 views

Train CNN-RNN network for multi label video classification with sliding window technique

I’m implementing a model in which a CNN model is used to extract feature sequences from videos , and RNN is used to analyze the generated feature sequences, and output a multi label classification ...
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0answers
28 views

Autoencoder for clustering

I would like to know if the following strategy could work. I want to cluster images, using the following 2 steps: Reduce image dimension with autoencoder apply clustering algorithm like k-means I ...
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2answers
14 views

How to use a deep learning algorithm to cluster image *styles* in an unlabeled data set?

I have a hard problem, and I'd be interested in hearing people's thoughts. I have a set of images depicting a large variety of phenomena, but only a few styles. The images are already labeled by ...
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1answer
45 views

I have 32k black and white images. Want to do clustering on them

As the title says I'm trying to do clustering on a set of black and white images. These images are all 200x200 with black dots on a white canvas Example pics here (These are not actual photos from the ...
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1answer
28 views

Clustering with geolocation (lat/long pairs) attributes

I am trying to cluster customer behavior based on where they shop given by lat/long pairs. I also have other numeric attributes such as volume, average amount spent, etc. I am considering using ...
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12 views

Extract data from mainly unstructured sets and derive risk metrics out of those

I have the following question (this was a real life example): Q: Extract data from mainly unstructured sets and derive risk metrics out of those. From what you know or imagine about the data ...
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1answer
120 views

Isolation forest sklearn contamination param

I'm working on an unsupervised anomaly detection task on time series using isolation forest algorithm. I'm developing in Python, more in detail using sklearn. I found out a lot of examples on this, ...
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15 views

Design / Choice of Autoencoder to classify temporal pattern in images

Suppose I have a temporal stack of images of shape $m \times n \times k$ where shape of each image is $m \times n$ and $k$ represents the temporal dimension. In this context, I am trying to detect and ...
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9 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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1answer
29 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
16 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
31 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
33 views

approach for predicting machine failure using maintenance history

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
36 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
32 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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41 views
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23 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
37 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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4answers
31 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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3answers
79 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
16 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
41 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
126 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
21 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
26 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
34 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
135 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
113 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
24 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
35 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
125 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
299 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
143 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
134 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
822 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
48 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
140 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
297 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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1answer
310 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 ...