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Questions tagged [clustering]

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common ...

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Bag of Visual Words

What I am trying to do: I am trying to classify some images using local and global features. What I have done so far: I have extracted sift descriptors for each image and I am using this as my ...
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Is Minimax Linkage a Lance-Williams hierarchical clustering?

I found the following article on "Hierarchical Clustering With Prototypes via Minimax Linkage". It is stated in Property 6 that Minimax linkage cannot be written using Lance–Williams updates. A ...
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Clustering for high dimensional data

I am having a data set with 52 variables .Most of them are having zeros it resembles like sparse matrix .How to cluster this kind of data and is there any special types of clustering? Here I am ...
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Clustering with Replicator Neural Network

I'm trying to cluster an unknown set of data with a replicator neural network. The number of clusters is determined by the number of neuron units in the middle layer, multiplied by the number of steps ...
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clustering plus linear model versus non linear (tree) model

a team has to create models that predict the cost of deploying a machine over time. This is a regression problem. The team is further divided into two groups, A and B. Group A puts lots of ...
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Fixed-radius range search in non-Euclidean space

I'm trying to find an indexing data structure most suitable for my metric space: set of IP network related data (IP addresses, ports, TCP flags, ...), distance function is continuous, non-Euclidean ...
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Robustness of ML Model in question

While trying to emulate a ML model similar to the one described in this paper, I seemed to eventually get good clustering results on some sample data after a bit of tweaking. By "good" results, I mean ...
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clustering credit card accounts based on their balance trajectories

I am trying to cluster credit accounts based on the shape of their balance trajectories over the next 36 months, to identify the different types of shapes possible in the portfolio. Here is how I am ...
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Computing spectral gap of p-laplacian, p > 2

I'm looking for code allowing computation of the spectral gap of a graph p-laplacian with p > 2, i.e. the second largest eigenvalue. See http://www.ml.uni-saarland.de/code/pSpectralClustering/...
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Clustering events in a sequence.

I've a sequence of recurring events I want to group together into representing different operation activities of the underlying process. 1) These events might potentially have an order in their ...
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143 views

When to use cosine simlarity over Euclidean similarity

In NLP, people tend to use cosine similarity to measure to document/text distance. I just want to hear out what do you think for the following two cases, cosine similarity or Euclidean? The task is ...
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How do I choose number of clusters when Eigengap heuristic suggest 1 for spectral clustering?

Eigengap heuristic Method suggest number of clusters k is usually given by the value of k that maximizes the eigengap (difference between consecutive eigenvalues). I plotted the Eigenvalue ...
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t-SNE plotting DBSCAN clustering results very scattered issue

We are trying a DBSCAN clustering model on our 30,000 samples with 15 features each. We tuned the epsilon parameter small enough to make sure the radius of the clustering circle is small while it does ...
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How to choose the optimal k in k-protoypes?

To analyze a dataset from banking I have both numerical and categorical values. I transform them to analyze with k-prototypes. The original dataset: The modified dataset: E.g.: Job (for 1 to 12 '...
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Mixed geospatial and categorical clustering

I'm working on a project that seeks to identify clusters in urban development based on location (in lat/lon) and a categorical variable (what the particular site is zoned for). Ideally, the analysis ...
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351 views

Implement gaussian mixture model with stochastic variational inference

I am trying to implement Gaussian Mixture model with stochastic variational inference, following this paper. This is the pgm of Gaussian Mixture. According to the paper, the full algorithm of ...
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336 views

graph database and its clustering

An undirected graph represents a database where nodes of the graph represent tables, edges represent the joiner columns. There are 100 databases( it means, 100 undirected graphs). We have to build ...
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Accept any suggestion to create training data from correlation matrix to find odd one out to identify difference in variation

I have N time varying feature vectors obtained by recording different parameters over time.This results in N*N similarity matrix which contains one to one correlations value for each feature. We need ...
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Selecting the number of hashes for minhash? Working with extremely sparse data and want more collisions

I'm attempting to use minhash to generate clusters and similarities, and I am primarily using ideas from these resources. http://www2007.org/papers/paper570.pdf https://chrisjmccormick.wordpress.com/...
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Finding dominating attributes with in the clusters generated

I am having a dataset of customers where each customer is represented as some feature vector and I am applying K-means algorithm to this dataset. On the basis of those features, I can abstract and ...
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How to apply K-Medoids in many CFG?

I am having around 1000 DAG(Directed Acyclic Graph) of different files showing java.io.BufferedReader usage. Following is representation of one of the graphs ...
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Spatial clustering of data points on a grid to obtain variable resolution map with constant statistical confidence

I have a grey valued image which is calculated as the mean of a series of images. The value of each pixel is therefore associated to a standard error. The pixel values and the relative standard error ...
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Discovering dis-associations between periods of time-series

I'm interested in discovering some kind of dis-associations between the periods of a time series based on its data, e.g., find some (unknown number of) periods where the data is not similar with the ...
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Datasets for Weighted graph clustering with detailed ground truth

I want to compare a new algorithm for clustering nodes of weighted graphs (directed or undirected, both are fine) to some previously proposed algorithms but in order to do the comparison I need more ...
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Autoencoder ambivalent about order of input data?

The problem I'm working to solve is this: Given a musician's prerecorded free-form playing. I want to analyze each of the individual notes to determine how "in-rhythm" it is. See the graph in the ...
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Classifying variable types on a list of variables

I have a list of around 700 variables which I need to perform a variable cleanup on. What complicates things is there are different numeric codes which flag an invalid value and these differ by the ...
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What is the difference between K-Means & Self Organized Maps?

It seems they both perform clustering. They both reduce the dimensionality of the input data and classify further inputs based upon their distance/similarity to the center points. These points then ...
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Is my data good for (DBSCAN) clustering?

I have a particular dataset consisting of 50k elements with 40 features each. I want to try to cluster the data as it is, without any dimensionality reduction. The main algorithm I am considering is ...
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Automatic Semantic Clustering and Tagging of sentences using NLP

NLP Analysis for keyword clustering I have a set of keywords for search engines and I would like to create a python script to classify and tag them under unknown categories. To make it clear I ...
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Limitations while using orange for clustering

I have tried clustering using kmeans in Orange but it looks like there are certain limitations as listed below, - Supports up to 5000 records only - no. of clusters can be only 30 Can someone please ...
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Clustering/ Classifying users based on sequence of action and time

I have some user data where each user has certain pattern of being at different places for some time. I want to create a model which will cluster/classify these users based on these patterns and time ...
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Find words related to high or low score

I am working on text analysis problem. Person X can log in his goals and his actions to achieve his goal. Also their score is calculated based on some formula to measure progress of the goal. For ...
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Categorical data with order and blanks, is frequent dataset or k-modes a better option?

I have a dataset that's purely categorical: for each item it's ranked across a set of attributes, whether it's easy, moderate or difficult. But there are blanks if the item doesn't have the ...
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Conceptual clustering with sklearn?

How can I perform conceptual clustering in sklearn? My use case is that I have English Wikipedia articles that I'm doing unsupervised learning on (tfidf -> truncated svd -> l2 normalize), and I'd like ...
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k- means clustering on Markov chain trasition probability

I have data set of 50 students. I want to cluster them on their sequential data ( While doing a job they followed multiple sequences A, B, c total 7 stages). I am planning to apply k-means clustering ...
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PCA Reduction resulted in an elliptical form

I have a dataset with 19 features (columns). I normalized them using sklearn.preprocessing.normalize then I used PCA to reduce them to 2 components for plotting ...
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296 views

Perplexity increasing on Test DataSet in LDA (Topic Modelling)

I was plotting the perplexity values on LDA models (R) by varying topic numbers. Already train and test corpus was created. Unfortunately, perplexity is increasing with increased number of topics on ...
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138 views

Predict User Demographics from location based social networks

I am currently working on an lbsn (localization-based social network) system and i need to predict the user's age and gender. Every time a user enters a venue, the system creates a "check-in" with ...
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Finding lookalike for large number of users

We have a large user-base, within which we want to find lookalikes, around 25-30 Million, for the users, we have data such as show liked by the user, genre liked etc. What we need to do is ...
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Find irregular bounding shape of 3D particle distribution

I have some 3D particle data e.g. (x0,y0,z0) (x1,y1,z1) (x2,y2,z2) ... I want to find the irregular bounding shape of the distribution. The image below shows an ...
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Density Tree - What is the x axis?

I am looking at density trees The intuition about the y-axis is clear: the tree indicates the modes which then merge at merge height: $$m_p(x,y) = \sup{t: \exists C \in \textit{C} \quad s.t. \quad x,...
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SOM initial values for learning rate and neighborhood sigma

I am using SOM (Self-Organizing Maps) of Kohonen, or more specifically, the MiniSom, found here to cluster and visualize my data. As you can see in the above site, the example given is: ...
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Seeking Appropriate Clustering Algorithm

I'm analyzing the GDELT dataset and I want to determine thematic clusters. Simplifying considerably, GDELT parses news articles and extracts events. As part of that, it recognizes, let's say, 250 "...
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Looking for an algo transforming numerical attributes into categorical attributes -cleverly

I created an algorithm which works on categorical attributes. The input data comes with categorical attributes, but numerical ones as well. How can I apply a pre-processing which transforms the ...
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46 views

Clustering product searches into products

I've been tasked with clustering searches from our website into the different searches for the same product. e.g. such different searches for the same product may be "product name" / "product name 1st ...
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597 views

Grid Search on Unsupervised Sklearn Clustering?

I am trying to use clustering algorithms in sklearn and am using Silhouette score with cosine similarity as a metric to compare different algorithms. My question is due to the varying hyperparameters ...
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385 views

k-means python implementation: unclear\wrong result

I have implemented two type of clustering in python, using SciPy: one with hierarchical approach, and the other one with k-means. In each cases I have used as input a two dimensional array X (...
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45 views

Clustering objects defined by vector

I have a set of objects (98 total). I need to cluster these objects based on their pairwise distance. Each pair contains approximately 2000 values (not consistent). This is what I have done so far. ...
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55 views

What is the best way for cluster sentences using information provided by a POS tagger?

I have a small set of sentences (around 20) and I want to cluster them. Only features I have from every sentence is the output of a POS tagger that I apply to them before. How could I approach this ...
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216 views

Outlier Detection

I have a dataset which has two class. It has 13 features. They are values which are sent from 13 sensors. Label is True or False. When I use mad outlier detection, when the label is false(really there ...