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).

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What is the best practice to resolve optimize cluster numbers for ODKM in Python? [closed]

I'm experimenting with an unsupervised statistical-based outlier detection so-called ODKM on top of the KMeans clustering algorithm. During my experiments, I noticed that there is a limit for the ...
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Clustering using k-medoids

This is the program function code for clustering using k-medoids ...
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2answers
191 views

Multithreaded implementation of K-means clustering algorithm in Java

Hello I have written a multi-threaded implementation of the K-means clustering algorithm. The main goals are correctness and scalable performance on multi-core CPUs. I expect to code to not have race ...
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3answers
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Finding the closest point to a list of points

I'm trying to find the closest point (Euclidean distance) from a user-inputted point to a list of 50,000 points that I have. Note that the list of points changes all the time. and the closest distance ...
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N closest points to the reference point

Here is working code to get N closest points to some reference point. Please help to improve it, specifically by commenting on my use of std algorithms and ...
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539 views

My approach on nextjs cluster with socket.io

I'm interested in feedback on my approach to handling cluster on Next.js SSG app with express hosted on Heroku. The app is working however, please let me know if this is the wrong approach or if you ...
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1answer
84 views

Calculation of the Distance Matrix in the K-Means Algorithm in MATLAB

Purpose of the code : To assign the corresponding label of the centroids to the points which are close to it. Below is a graphical (2D) example. Variable X is a matrix, rows represent the points, ...
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1answer
102 views

A Tiny Nearest Neighbor Classification Implementation in C#

I am practicing to implement the KNN classification tool in C#. The basic point structure is constructed by the class Point, and there are two members in ...
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1answer
113 views

K-clustering algorithm using Kruskal MST with Disjoint Set in place to check for cycles

here below a working implementation that finds the minimal distance between k(set =4 below) clusters in a graph. I have doubts mainly on the implementation of the ...
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1answer
104 views

R function to generate predictions from ratings

I am trying to improve the run time of a program I wrote in R. Generally, what I am doing is feeding a function a data frame of values and generating a prediction off of operations on specific columns....
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1answer
2k views

K-means clustering implemented in Python 3

Here is the classic K-means clustering algorithm implemented in Python 3. My main concern is time/memory efficiency and if there are version specific idioms that I could use to address issues of the ...
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2answers
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K-means clustering in Python

The following code uses scikit-learn to carry out K-means clustering where \$K = 4\$, on an example related to wine marketing from the book DataSmart. That book uses excel but I wanted to learn Python ...
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1answer
3k views

Closest Pair algorithm implementation in C++

I had been working on the implementation of closest pair algorithm in a 2-D plane. My approach has been that of divide and conquer O(nlogn) : ...
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3answers
831 views

Nearest Neighbour classification algorithm

The following code is from a university assignment of mine to write a classification algorithm (using nearest neighbour) to classify whether or not a given feature set (each feature is the frequency ...
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1answer
196 views

Implementation of K-means

I have recently built a class that is an implementation of kMeans from scratch. I believe there is room for improvement and I would happily receive some feedback. The project can be found at: https://...
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1answer
2k views

KNN pipeline w/ cross_validation_scores

Using the wine quality dataset, I'm attempting to perform a simple KNN classification (w/ a scaler, and the classifier in a pipeline). It works, but I've never used ...
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1answer
786 views

k-means implementation in python

This is the first mini-project that I'm working on python, where I implement k-means. I'm planning to parallelize it as soon as I've written a good serial version. Code description: Below you will ...
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1answer
365 views

Schelling's model of Segregation Python implementation with Geopandas

If you don't know what is Schelling's model of segregation, you can read it here. The Schelling model of segregation is an agent-based model that illustrates how individual tendencies regarding ...
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1answer
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OpenCV 3: Using k-Nearest Neighbors to analyse RGB image

I'm new to computer vision and numpy. I wrote a simple script to seperate red, green and blue colors from the original image by using the kNN algorithm. After reading through some numpy tutorials, I'...
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Grouping sorted coordinates based on proximity to each other

I created an algotrithm that groups a sorted list of coordinates into buckets based on their proximity (30) to one another. Steps: Create a new key with a list ...
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96 views

Machine learning, kNN and Naïve Bayes algorithm

This is the task I am working on: In this assignment you will implement the K-Nearest Neighbour and Naïve Bayes algorithms and evaluate them on a real dataset using the stratified cross validation ...
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1answer
69 views

Getting hex colours from a image

I am trying to get the hex colours from an image. The problem I am having is that for some reason randomly the code causes high CPU usage, which times out the browser and I am not sure how to optimise ...
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1answer
1k views

Cosine Similarity on Huge Dataset

I have a very large data file full of movie ratings that I am looking at for work. I wanted to do this in a clean and very effective manner. The ratings file contains on a per column by column basis: ...
2
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1answer
539 views

Welford's online variance calculation algorithm for vectors

I'm developing a face recognizing application using the face_recognition Python library. The faces are encoded as 128-dimension floating-point vectors. In addition to this, each named known person ...
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0answers
268 views

Locality Sensitive Hash (similar to k-Nearest Neighbor), in Python+Numpy

I've tried implementing Locality Sensitive Hash, the algorithm that helps recommendation engines, and powers apps like Shazzam that can identify songs you heard at restaurants. LSH is supposed to run ...
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5answers
20k views

Grouping consecutive numbers into ranges in Python 3.2

The following is a function that I wrote to display page numbers as they appear in books. If you enter the list [1,2,3,6,7,10], for example, it would return: <...
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2k views

A Simple K-Means Cluster Analyzer v0.1

[NOTE] This question can be depreciated in favor of version 0.2. This code works well. This my first attempt at creating a robust, computationally lean K-Means Cluster analyzer. I first saw this ...
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1answer
43 views

Analyzing distances between clusters of orders [closed]

I wrote the Python class below, which does what I want it to do, but the data structure is a mess. Was wondering if there was a better structure I could use to get the same results but with better ...
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42 views

Cluster correlations for many (> 500k) features by threshold

I have a matrix with roughly 500,000 features that I'd like to correlate and find features that have correlations >= 0.90, then cluster these into one group. Is there an efficient algorithm to do this?...
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2answers
239 views

Top k closest pairs in a set of million 128-dimensional points [closed]

I have a set of 1 million points in 128-dimensional space. Among all trillion pairs of the points in the set, I need to get a subset of 100 million pairs whose cosine distances are less than that of ...
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1answer
137 views

Efficiently determining maximum allowed euclidean distance between lists of colors

I was recently tasked with determining which hex RGB colors in list color_list are nearest to each hex RGB color in list target_colors, using euclidean distance as the measuring stick, and only ...
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1answer
5k views

k-means using numpy

This is k-means implementation using Python (numpy). I believe there is room for improvement when it comes to computing distances (given I'm using a list comprehension, maybe I could also pack it in a ...
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1answer
75 views

Simple natural language classifier

This program estimates the likelihood for a string to belong to a certain natural language by computing the cosine similarity between an input string's and several natural languages' letter frequency, ...
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1answer
333 views

Haskell K-means implementation

The following is a Haskell backend to a K-means visualisation: I have omitted the API code (exists in a separate module), the relevant endpoints simply call ...
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1answer
272 views

Predicting outliers in network data [closed]

I'm implementing KNN algorithm to predict outliers in network data which contains the following columns: source IP address, source port number, protocol and total bytes transferred. To achieve this, I'...
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87 views

Regression on Pandas DataFrame

I am working on the following assignment and I am a bit lost: Build a regression model that will predict the rating score of each product based on attributes which correspond to some very common ...
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1answer
264 views

K-Nearest Neighbors in pure Python

I want a general criticism on this code. Using external modules is not an option, I can only use what comes with CPython. ...
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1answer
1k views

Determining the similarity between two documents

I've made some code that reads in text files (which hold quite large vectors of word frequencies), which in turn stores each index of a vector within an ArrayList ...
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2answers
3k views

Removing neighbors in a point cloud

I have written a program to optimize a point cloud in dependency of their distances to each other. The code works very well for smaller number of points. For 1700 points it takes ca. 6 minutes. But I ...
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2answers
3k views

K-Means image segmentation algorithm

I am a new C++ programmer and I have some experience in Python and C but I was almost completely self taught (I learned C++ with OpenClassrooms). I would like to learn the conventions and how things ...
4
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1answer
158 views

Grouping orders by similarity (cluster) of their items

My task is to write an algorithm for grouping list of orders into batches, each batch consisting of 4 orders. Orders are grouped by similarity of their items, which means the more items from Order X ...
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0answers
227 views

Calculation of clustering metric in Python

When I try to run the following code for arrays with more than 10k elements, it takes hours and I don't know how to make it in the most efficient way. Any ideas? ...
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0answers
7k views

Fuzzy c Means in Python

This is my implementation of Fuzzy c-Means in Python. In the main section of the code, I compared the time it takes with the sklearn implementation of kMeans. ...
12
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1answer
21k views

String Matching and Clustering

I have a pretty simple problem. A large set of unique strings that are to various degrees not clean, and in reality they in fact represent the same underlying reality. They are not person names, but ...
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0answers
509 views

Compute distance matrix using DTW acceptable for scipy.cluster.hierarchy

I am new to both data science and python. I have a dataset of the time-dependent samples, which I want to run agglomerative hierarchical clustering on them. I have found that Dynamic Time Warping (DTW)...
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0answers
139 views

Cluster tweet texts

I am using using the following code to cluster tweet texts. The input is a dictionary containing tweet-id and tweet text as key value pairs. Example: ...
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1answer
1k views

Pole (Hackerrank)

Problem Description Kevin was thinking about telephone poles and came up with an idea for a fun programming challenge. There are n telephone poles ascending a mountain and each pole has a weight ...
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2answers
2k views

Clustering nodes with Hamming distance < 3

I want to speed up the following code, which is from an algorithm class. I get a list of 200000 nodes where every node is a tuple of the length of 24 where every item is either a 1 or 0. These ...
3
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1answer
586 views

R - Outlier Detection Algorithm

I am trying to implement an algorithm for detecting outliers in R and I am pretty new to the language. The outlier algorithm is described in this paper in detail on page 10-11, but to summarize it ...