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Questions tagged [unsupervised-learning]

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

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Machine Learning with sometimes missing data

I'm trying to do an indoor locationing system based on my RSSI signal on my routers, I'm sniffing my network so I know what's the RSSI of my phone related to my routers antennas (I have 5 antennas all ...
c4b4d4's user avatar
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3 answers
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What's the fastest clustering package in Python?

I'd like to perform clustering analysis on a dataset with 1,300 columns and 500,000 rows. I've seen that clustering algorithms are available in SciKit-Learn. But I'm worried that the algorithms will ...
Connor's user avatar
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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 ...
Giordano's user avatar
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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. ...
Anchika Agarwal's user avatar
2 votes
2 answers
193 views

Using a K-NN Classification Approach for Time Series Data?

I have a dataset which contains time-series data of water flow over time. I have a flow meter connected to a kitchen faucet, and I am trying to cluster or classify specific water usage events. The ...
Gary's user avatar
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Can we fine-tune a model on the same dataset which it is pretrained on?

So I was reading this paper (about a use case of pretraining then self-training) which got me thinking - suppose I pre-train a model on a particular dataset, then fine-tune it again on the same ...
neel g's user avatar
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2 answers
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Document similarity

I have close to 50000 documents in plain text format. Is there a way in which I can group similar documents together? Similarity mostly here is the content similarity. Will transforming the text ...
praneeth's user avatar
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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 ...
A.White's user avatar
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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 ...
Rodolphe LAMPE's user avatar
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2 answers
1k 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 ...
Galuoises's user avatar
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Which is the best Machine learning technique for this Load forecasting problem?

I am trying to use Machine Learning to predict the load of a residence at any point in time for a whole year. I have past data pertaining to that house. So I have the training data and I need the ...
Aashiek Hariharan's user avatar
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1 answer
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Does Non Negativity Constrains increases the estimation error

I have been working with Tensor and matrix Non negative constrained algorithms. I have never seen a non negative constrained algorithm (ex. Non Negative Tucker Decomposition NTD) with error that is ...
Mour_Ka's user avatar
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Why my loss is negative while training SAE?

I am using loss='binary_crossentropy' here is my code: I tried to increase number of training image and Epoch ,but that did not help me. ...
sp_713's user avatar
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How to improve the preservation of the global data structure in UMAP?

I have a dataset, where the features are comprised of points arranged in a regular grid on a simplex. Each of these points are defined as follows: A point $\mathbf{x}$ on the simplex can be ...
pmu2022's user avatar
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1 answer
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Using k-means to create labels for supervised learning

I want to know if the following is a valid approach to create labels, if I have measurements under some conditions, and the conditions are similar but never exactly the same. This doesn't correspond ...
Seb001's user avatar
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Choosing a distance metric and measuring similarity

I am trying to decide which particular algorithm would be most appropriate for my use-case. I have dataset of about 1000 physical buildings in a city with feature space such as location, distance, ...
kms's user avatar
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Choosing attributes for k-means clustering

The k-means clustering tries to minimize the within-cluster scatter and maximizing the distances between clusters. It does so on all attributes. I am learning about this method on several datasets. To ...
Borut Flis's user avatar
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1 answer
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Adding anomalies to the Dataset

Recently I have been trying different Scikit-Learn anomaly detection clustering methods, like DBSCAN Isolation Forest. Based on how many training data I use, how I tweak on the algorithms ...
E199504's user avatar
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2 answers
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What is a manifold for Unsupervised Learning?

I've been watching Dr. G. Hinton lectures on Neural Networks in Machine Learning, and in one of the lectures he explains what the goals of Unsupervised Learning are. I am having trouble ...
Stefan Radonjic's user avatar
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2 answers
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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 ...
Kevin's user avatar
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1 answer
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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 ...
Arav's user avatar
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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 ...
DonCappie's user avatar
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1 answer
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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 ...
rj487's user avatar
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2 answers
103 views

Which machine learning algorithm to choose?

I want to choose an unsupervised algorithm which learns to predict $n$ outputs from the data, for eg. 4 coordinates (pixels) in an image. What algorithm should I choose? I think it's a 2-class ...
ab123's user avatar
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Learning Football Player Stats like FIFA's by only the game result [closed]

It is a general question on how to learning representation of one entity but the dataset is mixed with a lot of other entities, which their statis are always waiting to be learnt. The question is ...
palazzo train's user avatar
2 votes
1 answer
227 views

statistics or robust statistics for identifying multivariate outliers

For the single variate data sets, we can use some straightforward methods, such as box plot or [5%, 95%] quantile to identify outliers. For multivariate data sets, are there any statistics that can be ...
user288609's user avatar
2 votes
1 answer
229 views

What to do with stale centroids in K-means

When I run Kmeans on my dataset, I notice that some centroids become stale in the they are no longer the closest centroid to any point after some iteration. Right now I am skipping these stale ...
foboi1122's user avatar
  • 123
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1 answer
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Im looking for good neurons silmilarity metric

Recently I managed to create simple neural network visualization, to help to understand how neural network works on the signal level. I also wanted to arrange neurons by similarity cause I was ...
dismedia's user avatar
2 votes
1 answer
151 views

Clustering ordered categorical data

Suppose I have a list of, say, 100 countries, as well as their respective historical sovereign credit ratings as such ...
Carl's user avatar
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2 votes
1 answer
1k views

Clustering on binary data

I am working on clustering on binary data which has 25 features, sample Feature 1 Feature 2 Feature 3 ...... Feature 25 1 1 0 0 011101 1 2 0 1 0 010011 0 3 1 0 1 101001 1 and I have used the ...
Ethan's user avatar
  • 21
2 votes
1 answer
491 views

Clustering using both text and numerical features

I have a dataset that contains 2 types of features, one is generated from doc2vec and one is numerical feature. I would like to perform clustering analysis on them. However, due to the size of doc2vec ...
E.TTT's user avatar
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1 answer
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Recommender/Clustering data to support a hypothesis. Is this a valid use-case for unsupervised ML?

I have a dataset where some items have been labelled (categorized into 4 classes [A,B,C,D]). However, there is a vast majority of the dataset which has not been labelled. My hypothesis is that there ...
Alex Dore's user avatar
2 votes
1 answer
262 views

K-Medoid Clustering with Point Weights

I asked the same question at Cross Validated, here I implemented a K-Medoid clustering algorithm recently; I have a number of points $x_1, ..., x_n$ which have various properties and a distance ...
Joseph Doob's user avatar
2 votes
1 answer
65 views

What type of consideration can be made using clustering?

I am clustering my data to see how information look like and which group may be identified. Since clustering is an unsupervised algorithm, I cannot test the accuracy of the classification. So I was ...
Math's user avatar
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2 answers
996 views

How to get the probability/closeness of a sample belonging to a specific cluster?

I'm new to this so please let me know if my logic of comparing cosine similarity and k-means is incorrect I got a set of ...
Jaskaran Singh Puri's user avatar
2 votes
1 answer
36 views

Topology of Data

Do every data resembles some form of space like manifold? Meaning, do geometry of data (especially the big data) is a manifold? Is every data embedded to some sphere? If that is so, can we also say ...
Phoenix13's user avatar
2 votes
1 answer
64 views

Are there any examples other than anomaly detection where unsupervised deep learning could be useful?

I am new to deep learning and its concepts. After reading a while I understood that unsupervised deep learning techniques usually try to reconstruct the input data(probably with less number of ...
Sushodhan's user avatar
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1 answer
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Clustering Weekday Weekend Data and Multicollinearity

Hi I have data of weekday and weekend step counts in which I extracted metrics from them such as the wd steps, we steps, standard deviation of wd steps, standard deviation of we steps and so on... <...
TYL's user avatar
  • 171
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1 answer
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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 ...
Hun Cho's user avatar
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2 votes
4 answers
1k 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 ...
greyelf's user avatar
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2 votes
4 answers
679 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 ...
ripevik's user avatar
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2 votes
1 answer
115 views

Is PCA (by eigendecomposition) or SVD better in decorrelating the predictors of a machine learning model?

Is there any reason to think that SVD is better than PCA (by eigendecomposition) in decorrelating the predictors of a machine learning model?
Outcast's user avatar
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2 votes
1 answer
212 views

Is train/test-Split in unsupervised learning of neural network necessary?

I am using autoencoder for anomaly detection in warranty data. It is unsupervised. I calculate the reconstruction error by the model and the records with high reconstruction error value is considered ...
Ashwini's user avatar
  • 235
2 votes
1 answer
117 views

Given data that is labeled as outliers, how can I classify data as outliers?

I have a dataset that is a mixture of sparse binary features and quantitative features. I only have definite outliers labeled. How should I approach trying to classify unlabeled data? I considered ...
Halbort's user avatar
  • 123
2 votes
1 answer
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How can we define missing rating in recommender system?

I was reading about collaborative filtering where we need to pass (user, item and rating) in case of matrix factorisation (SVD). Now, my question is given data of ...
Gaurav Gupta's user avatar
2 votes
1 answer
1k views

Outlier detection on categorical network log data

I am working with a completely categorical network log data that consists of source ip address, destination ip address, source port, destination port, protocol. Data Preprocessing performed : ...
iprof0214's user avatar
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2 answers
6k views

Multidimensional Dynamic Time Warping Implementation in Python - confirm?

I believe that I implemented MDTW in python here but I don't know if I did it correctly. The results seem intuitive. Can someone look at this code and tell me if you see anything wrong? A lot of the ...
pennydreams's user avatar
2 votes
1 answer
285 views

What is the difference between Slow Feature Analysis (SFA) and a Moving Average?

I have started to read more about Slow Feature Analysis and I was wondering how SFA differed from simply taking a moving average? The linked article suggests, "SFA is an unsupervised algorithm that ...
Gary's user avatar
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2 votes
1 answer
1k views

How hidden layer is made binary in Restricted Boltzmann Machine (RBM)?

In RBM, in the positive phase for updating the hidden layer(which should also be binary), [Acually consider a node of h1 ∈ H(hidden layer vector)] to make h1 a binary number we compute the probability ...
Born2Code's user avatar
  • 347
2 votes
1 answer
119 views

Neural Network: how to utilize weakly-/unsupervised data to improve supervised network?

Let's consider one has built a fully-supervised neural network for some task, e.g. localizing an object in various scenes. As you can imagine, it is quite time-consuming to label data: one has to ...
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