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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Given a collection of points on a 2D plane, find the pair that is closest to each other
Full disclosure: I'm working on this for an online course. However, my goal is really just to get a pointer to where the issue is.
The goal is to implement the closest points problem, that is, given ...
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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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Single-pass clustering algorithm for sparse matrices
I have written single pass clustering algo for reading sparse matrices passed from scikit tfidfvectoriser but the speed is king of average for medium size matrix. How can I scale for large size ...
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2answers
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How to speed up execution of single pass clustering algorithm?
This is my code for clustering 111272 by 29987 tfidf vector but it is taking long time .How can I speed up the code execution. The tfidf matrix is sparse and works well for 10000 records.
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1answer
88 views
Clustering points on a sphere
I have written a short Python program which does the following: loads a large data file (\$10^9+\$ rows) where each row is a point on a sphere. The code then loads a pre-determined triangular grid on ...
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1answer
118 views
KNN algorithm implemented in Python
This is the first time I tried to write some code in Python.
I think it gives proper answers but probably some "vectorization" is needed
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148 views
Implementation of DBSCAN in C++
I've recently just finished my implementation of a DBSCAN in C++ for a machine learning framework. I've tried to follow the pseudocode implementation on Wikipedia as best I could. I also found some ...
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3answers
426 views
Selecting kids for a Christmas play with similar heights
I am doing this problem on SPOJ:
My kid's kindergarten class is putting up a Christmas play. (I hope he gets the lead role.) The kids are all excited, but the teacher has a lot of work. She has ...
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65 views
Implementation of a KNN in OCaml
I wrote the following implementation of the k-nearest neighbor algorithm (for a binary classification task). I am not familiar with OCaml's built in functions, I have the feeling that some of them ...
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0answers
123 views
Sentences Clustering - Affinity Propagation & Cosine Similarity - Python & SciKit
I am looking for advices regarding my code. I am interested about the correctness, legibility and minimality of the solution.
...
5
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0answers
84 views
K-Means clustering in Python2
I've implemented the K-Means clustering algorithm in Python2, and I wanted to know what remarks you guys could make regarding my code. I've included a small test set with 2D-vectors and 2 classes, but ...
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0answers
20 views
Closest points using Rabin randomizing approach
I was told to use the following Rabin algorithm to find the shortest distance between 2 points in 2D:
Randomly choose sqrt(n) and brute force to find the closest ...
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0answers
82 views
Find k nearest points
I'm working on a problem to select k nearest points for a given point. Any advice for bugs, improvements are appreciated, including general advice to implement find nearest k points.
My major idea is ...
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1answer
55 views
Zip code reduce function
My task is to write a function that would take an array of zip codes and spit out only the zip codes that do not qualify. A non-qualifying zip code will not exist in the database and does NOT have ...
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1answer
222 views
Cosine similarity computation
I have a matrix of ~4.5 million vector [4.5mil, 300] and I want to calculate the distance between a vector of length 300 against all the entries in the matrix.
I got some great performance time ...
3
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1answer
275 views
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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1answer
877 views
Finding closest pair of 2D points, using divide-and-conquer
I'm learning C++ as well as algorithms. Here's my implementation of finding the closest pair problem. I tried to minimize memory by using only iterators.
And points are being read from ...
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92 views
Divide-and-conquer approach for finding the closest pair of points
This is an algorithm for finding the closest pair of points on a 2d plane by dividing the problem by half recursively, as illustrated here:
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2answers
127 views
Calculating cooccurrence probabilities for pairs of words in a document
It is a 1.5 hour coding test, started the moment when the question was sent by email. My solution was done under the strict condition. I was not told anything before the test.
The question is about ...
5
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1answer
420 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 ...
3
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1answer
193 views
Clustering 16 million records in parallel
I have a dataset with 16 million rows and may increase upwards of 30 million. I am using the parLapply to run across three cores in R. But it's taking two days to ...
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0answers
190 views
Solving the Mining algorithm from HackerRank
I was working on this problem for a few hours last night and finally came up with a brute-force solution. The task is to report the minimum work necessary (sum of weight × distance) to relocate gold ...
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1answer
80 views
“Similar Destinations” challenge
I am currently solving the Similar Destinations challenge on HackerRank and am in need of some assistance in the code optimization/performance department. The task is to take a list of up to 1000 ...
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1answer
198 views
Similarity research : K-Nearest Neighbour(KNN) using a linear regression to determine the weights
I have a set of houses with categorical and numerical data. Later I will have a new house and my goal will be to find the 20 closest houses.
The code is working fine, and the result are not so bad but ...
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2answers
51 views
Getting the smallest snippet from content containing all keywords
This returns the smallest snippet from the content containing all of the given keywords (in any order). This provides the correct solution but I would like to know if it can be made more efficient.
<...
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0answers
86 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
242 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
84 views
Predict new ratings for each user based on their pearson correlation with other users
I am new to R and programming. I have a set of ratings for 45000 users and 40 odd movies. I need to predict new ratings for each user based on their pearson correlation with other users. I also need ...
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2answers
113 views
Grouping rectangles horizontally and vertically
As you can see the below code for each method is that same, except for the properties it uses. For example X vs Y and ...
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23 views
Applying kmodes on every “column wise subset” of a dataframe
I want to apply kmodes for 2 clusters on every possible combination of columns from a dataframe. Finally, I want to compare the clusters with another column that ...
2
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1answer
185 views
K-Means Clustering - F# Learning Challenge
Inspired by this blog I went on implementing my own version as a F# learning challenge. It turned out to be quite different than the original (but somewhat faster for large samples).
The first code ...
14
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1answer
372 views
Dynamic Colour Binning: Grouping Similar Colours in Images
This is a piece of code that implements an image-processing algorithm I came up with. I call it Dynamic Colour Binning. It's a fairly academic exercise that was more about providing a learning ...
6
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1answer
1k views
k-means clustering algorithm implementation
Here is my personal implementation of the clustering k-means algorithm.
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2
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1answer
333 views
Clustering similar tweets in a corpus
I am attempting to write a statistical program using an LDA model I've trained/created using Gensim. I am very new to Python and am a student level programmer. This current program is working and ...
5
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1answer
158 views
K-means clustering in Rust
I've implemented K-means clustering in Rust. It's my second Rust project (my first one is here: Randomly selecting an adjective and noun, combining them into a message)
I would like advice on ...
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1answer
47 views
Store and output hard-coded relationships among hosts
The following code has begun to smell, but I have not yet decided with what to replace it, other than, obviously, a database.
I made a very unsatisfactory workaround for my attempt to make ...
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1answer
93 views
DBSCAN in C++ for general and Android use
I've implemented a templated DBSCAN for general use. At the moment, it's going to be used on Android through the JNI. I used Wikipedia's pseudocode and a little bit of the DBSCAN paper for reference. ...
4
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1answer
343 views
PANDAS spatial clustering
I'am writing on a spatial clustering algorithm using pandas and scipy's kdtree. I profiled the code and the .loc part takes most time for bigger datasets. I wonder ...
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0answers
123 views
Cluster arrays according to similarity of key values
The below script will compare a set of arrays according to similarities between their key's values. For example, if the first 4 keys values of an array are equal to another array's first 4 keys values,...
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1answer
979 views
Implementing a fast DBScan in C#
I tried to implement a DBScan in C# using kd-trees. I followed the implementation from here.
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4answers
792 views
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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0answers
237 views
Depth First Search for percolation to find clusters in Go game
I have some questions about Depth First Search and whether I implemented it correctly. Below is a more thorough discussion. The graph in question is a randomly colored square grid (I use 3 colors). ...
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28 views
Collaborative filtering to group similar users and products
I'm doing product recommendation module based on collaborative_filtering.
The recommendation will be generated by users, ...
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152 views
C# port of data mining algorithm much slower than reference implementation
I was trying to implement the algorithm specified in this research paper (please ignore the math, since it's irrelevant to the question). This algorithm is very basic in formal concept analysis. The ...
4
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1answer
3k views
Implementation of KNN in R
I have implemented the K-Nearest Neighbor algorithm with Euclidean distance in R. It works fine but takes tremendously huge time than the library function (get.knn). Please point out the possibility ...
3
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1answer
103 views
Simple string-root detection in a string-family
(This problem is related to Simple string-split by root and sufix algorithm)
There are many ways to find a "common root" of a list of similar strings, that begins with the same substring... The ...
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2answers
239 views
Finding the maximum pairwise difference in a collection of colors
Note that this problem is equivalent to finding the longest line segment defined by any two points in a collection of 3D coordinates, which may be an easier way to visualize the problem, and is almost ...
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3answers
1k views
Finding clusters in a matrix
I got asked at an interview to write a program that, given a NxM matrix with zeros and ones, prints out the list of clusters of 1s. The clusters are defined as patches of 1s connected horizontally, ...
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0answers
744 views
Discretization of continuous attributes for automatic classification [closed]
Background
In machine learning, it's common to encounter the problem of making a decision as to which discrete category an object belongs to based on a set of continuous attributes. For example, we ...
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4answers
3k views
Aggregate array values into ranges
In five minutes I made a pretty ugly looking function. Can you help before I have to commit the code into history?
Requirements:
I would like a function that takes an array of numbers, and ...
