Questions tagged [unsupervised-learning]

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

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17 views

What is the difference between all the different types of learning within machine learning?

This is a question that is really hard to google, and the differences are confusing. Does anyone have good examples of the differences between them all? Supervised Learning Semi-Supervised Learning ...
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1answer
38 views

Anomaly detection - relation between thresholds and anomalies

I'm developing an anomaly detection program in Python. Main idea is to create a new LSTM model every day, training it with the previous 7 days and predict the next day. Then, using thresholds, find ...
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1answer
34 views

K-Means initialization

K-Means initializes the centroids randomly, but there are other methods to initialize. In this paper, http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf, they propose randomly choosing a data point ...
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20 views

Predict with some probability the day of the month paycheck received through daily transactions

I am using R to do machine learning. I have daily shopping expenditure data of individuals over a couple of months and my goal is to be able to identify through their shopping pattern the day of the ...
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24 views

Unsupervised Function Optimization using Input and Output for Loss Function?

I have some vectors {$\mathbf{X_1 ... X_n}$} and they are all of dimension 1 x N. Vectors {$\mathbf{X_1' ... X_n'}$} are also 1 x N and are related to {$\mathbf{X_1 ... X_n}$}, but the relation cannot ...
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56 views

Autoencoder clustering with a small dataset

I have a dataset which is relatively small (less than a 1000 samples). I run an autoencoder on a training set, and then check the reconstruction error on a validation set and stop training before it ...
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2answers
36 views

Clustering Small Text Descriptions

Im presented with a unique text classification problem. Im given a list of descriptions each containing 3-8 words. I know that there are some descriptions that are nearly the same, but the majority ...
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0answers
9 views

Error on prediction keras multi_gpu_model

I've an issue running a keras model on a Google Cloud Platform instance. The model is the following one: ...
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0answers
6 views

Is there any library available for Manifold learning using 'Diffusion map' in python?

I would like to use an unsupervised learning with technique called 'Diffusion map based manifold learning' in python. The original paper on diffusion map is here. I have already checkout pydiffmap ...
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0answers
14 views

Mixing unsupervised and supervised algorithms in image classification model

I am trying to replicate the general image classification model used in a paper that I cite later below. The following image is an extract from a paper that proposes a novel method of performing image ...
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2answers
25 views

customer segmentation with unbalanced data

I am trying to do a customer segmentation on my transactional data and I am struggling a little bit on the best approach. Since it is an unsupervised model I can throw it to any algorithm and get some ...
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1answer
19 views

How would you quantify an experience into a score without labeled data

How would you approach a scenario where you have to quantify an abstract notion like “customer experience” without having any labeled data? So basically what you have are bunch of variables that you ...
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1answer
18 views

Recommendation needed for unsupervised clustering on mixed data task

I have a task to perform unsupervised cluster analysis on mixed datatypes: images, physical and business measures – continuous and categorical. Businesswise: there are images of products and ...
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1answer
37 views

Doing predictive modeling on predicted value

It's a project that I'm working on. Here are the steps I took: I want to make a recommendation service based on the customer data. I first used a collaborative filtering method to get the recommended ...
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1answer
38 views

Anomaly detection thresholds issue

I'm working on an anomaly detection development in Python. More in details, I need to analysed timeseries in order to check if anomalies are present. An anomalous value is typically a peak, so a ...
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1answer
22 views

hierarchical clustering doesn't work as expected

I have a precomputed distance matrix. I'm trying to do an hierarchical clustering using scipy: ...
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0answers
14 views

Temporal outlier Analysis on sensor data

I am working to find anomaly/outliers in sensor data using unsupervised machine learning (without training dataset). I have around 20000 samples taken per minute of various sensors. I just need to ...
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0answers
7 views

Perform unsupervised anomaly identification with causation in Python?

I have a time series data, which contains information from various sensors measured at every 20 minutes interval. I would like to use information from all these sensors as features to a Deep Learning/...
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2answers
146 views

Suggestion for stacked modelling in machine learning

I have built several models on the training dataset and i am not happy with the results and I wish to club them all together and generate a new model, so here is my idea as i already have the results ...
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0answers
13 views

Choosing a distance metric and a clustering algorithm for time series

For every entity I have a corresponding time series which is built by a sliding window (win_size=7d, win_shift=3d, so we have overlapped windows) With every win-shift, we count how many users are ...
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10 views

Is it wise to include the target labels when a supervised learning problem is tackled as an unsupervised learning problem?

I have a problem which requires both a supervised and unsupervised learning approach. This is because I am trying to find some hidden clusters (if they even exist) beyond the labels of the dataset. ...
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1answer
26 views

Neural Networks for Unsupervised Learning

Why cannot we use neural networks for unsupervised learning problem. I do know that it can be used using the Kohenon’s Self Organizing Map (KSOM) but is this the only method that we can use or are ...
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1answer
12 views

Cluster evolution over time

I have a dataset of transactional data with customer ID and I want to segment the dataset into groups using cluster analysis. I'm interested in following the evolution of each cluster over time, but ...
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1answer
52 views

Risk score from Neural Network classifier (more than 2 categories)

I am trying to use a Neural Network to perform multiclass classification. The classes represent Insurance Risk Level. The most risky level is Level 1, the least risk corresponds to Level 10. The ...
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1answer
35 views

NER with Unsupervised Learning?

If we treated NER as a classification/prediction problem, how would we handle name entities that weren't in training corpus? For example, "James was born in England." James was labeled as a PERSON ...
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31 views

How to evaluate unsupervised KNN?

I'm creating a recommender system using an unsupervised nearest neighbors model to suggest similar publishers for a given publisher, advertiser combination. I'm wondering how to evaluate the model I ...
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0answers
29 views

How to tune / choose the preference parameter of AffinityPropagation?

I have large dictionary of "pairwise similarity matrixes" that would look like the following: similarity['group1']: ...
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0answers
26 views

Dataset Image creation suggestions

I am trying to create a dataset where the Text is mixed, e.g. " I love football" can be written as " I l()v3 F00tba11". The idea is that by using Tesseract I can find the pattern to match these two ...
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1answer
26 views

How to find similar points to a positive set when you don't have any negative set?

The task I'm used to do is the following. A client comes to see me with a set of clients (called positive companies) and he wants me to find other similar prospects. Usually, he also gives me a set of ...
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1answer
31 views

what arguments should I pass to dbscan or optic in order to divid the data in a specific way

I have thousands of very similar data set that needs to be divided in diagonal way to two groups. for example: and I tried to play with the argument of dbscan and optic as eps and minPoints and even ...
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2answers
26 views

Unsupervised Clustering for n-length word arrays

I have a series of arrays [Apple,Banana,Cherry,Date] [Apple,Fig,Grape] [Banana,Cherry,Date,Elderberry] [Fig,Grape] and I would like to build some clusters that ...
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3answers
36 views

Supervised clustering

I'm working on a clustering problem. I have a training set composed of sets of points where the clusters are known and I want to find the good clusters on a testing dataset. It's a kind of supervised ...
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1answer
20 views

Unsupervised Algorithm for hybrid data [duplicate]

I have a hybrid data that contains 15 categorical data and 4 continuous data. I need to implement a prediction on the data. So as I don't have any labeled data, I need to implement the unsupervised ...
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0answers
12 views

Pre-trained image recognition model to label new instances of retail products

Can you recommend unsupervised image recognition models that can be loaded/ transfered? My task is to provide labels to images of retail products (the N of classes is unknown and potentially huge (~...
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0answers
26 views

How to remove noise using morphological filtering

I have two groups of dots that both contain noise between them: The line that separates the two groups in the picture is diagonal in shape. I tried to use morphological filtering on this image to ...
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42 views

How to calculate Fuzzy C-Means problem by hand

I figured that this doubt next can interest another students like me and help others also that are trying to understand mathematically the fuzzy c-means mathematical mechanism already that some books ...
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1answer
39 views

Feature extraction in audio spectrogram

I have audio and its spectrogram of the words in English language. (A spectrogram is a frequency domain representation of a signal) Consider the words: chain, change, chair, chapter. As you can notice,...
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1answer
18 views

Are DBSCAN and dbscan from the sklearn.cluster package different?

I'm new to DBSCAN. I was looking at a few examples online and came across a few instances where the following lines were used while importing the dbscan module: <...
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1answer
32 views

trade offs between number of features with its score

I am running k-mean clustering on ~200000 samples. The dataset has in total 14 features. One feature is id and the rest are categorical. I have been playing with ...
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0answers
10 views

Using Unsupervised / Supervised Learning for RGB manipulation

I need to teach a model(or any substitute) for automatically adjusting the concentration of R, G, and B values until they make up white colour. i.e, tweak the RGB values (beginning from some arbitrary ...
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1answer
71 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
32 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
25 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
41 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
37 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
63 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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0answers
738 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
28 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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2answers
26 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
52 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 ...