Clustering Dataset Csv Download [PATCHED]

I would like to cluster a huge dataset into clusters based on similarity. How can I approach this problem? I have tried MinBatchK means and DBSCAN but I am not getting great results. The problem I am facing with MinBatchKMeans is I have to specify the number of clusters beforehand and with DBSCAN most are classified as Noise. Can someone please guide me on how to approach this problem? I am using TFIDF to convert text into vectors

For eg : When i am using a dataset of 80000 records it takes around 2-3 hours to form a cluster of size 500. This makes it very difficult to find optimal number of clusters with MinBatchK K means as i have to change values of K and then find out optimal number with approaches as Elbow Method. Can someone who has experience please let me know how to approach this problem ?

Clustering Dataset Csv Download

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The problem of clustering large datasets without knowing the number of clusters is something really hard to tackle, as pinpointed by the scikit-learn algorithm cheat-sheet. But some workaround exist which are dataset dependent, if you can provide some a priori on your data.

(In the following lines, I illustrate my point with the scikit-learn Python library syntax, but the statements are general and can be adapted to most of machine-learning libraries.) Depending on your answers to the above questions, you can try to apply the clustering on a subset of your data with model.fit(x_subset) in order to reduce the computation time, and then predict on the full dataset categories with model.predict(x_full). If the data is stationary, the class inference made on the subset will have a chance to work on the full dataset. If the cluster sizes vary a lot, you may need to go with hierarchical clustering (for instance with scipy hierarchical tools). This last tool may also be a good way to estimate the number of categories before going further with DBSCAN or any other strategy.

In any case, you are facing a problem often met in unsupervised machine learning. Note that you are trying to have an exploratory strategy (which is good), and if no exact solution emerges for your dataset, you will always learn something from your data by extracting intermediate-level information (like the answers to the three questions above) that can drive you in the next steps to cluster the full data.

I have a dataset consisting of 70,000 numeric values representing distances ranging from 0 till 50, and I want to cluster these numbers; however, if I'm trying the classical clustering approach, then I would have to establish a 70,000X70,000 distance matrix representing the distances between each two numbers in my dataset, which won't fit in memory, so I was wondering if there is any smart way to solve this problem without the need to do stratified sampling?I also tried bigmemory and big analytics libraries in R but still can't fit the data into memory

If you insist on using R, give at least kmeans a try and the fastcluster package. K-means has runtime complexity O(n*k*i) (where k is the parameter k, and i is the number of iterations); fastcluster has an O(n) memory and O(n^2) runtime implementation of single-linkage clustering comparable to the SLINK algorithm in ELKI. (The R "agnes" hierarchical clustering will use O(n^3) runtime and O(n^2) memory).

Oh, and if your data is 1-dimensional, don't use clustering at all. Use kernel density estimation. 1 dimensional data is special: it's ordered. Any good algorithm for breaking 1-dimensional data into inverals should exploit that you can sort the data.

You can use kmeans, which normally suitable for this amount of data, to calculate an important number of centers (1000, 2000, ...) and perform a hierarchical clustering approach on the coordinates of these centers.Like this the distance matrix will be smaller.

A python version of the package exists as well. The underlying algorithm uses segmentation trees and a neigbourhood refinement to find the K most similar instances for each observation and then projects the resulting neigbourhood network into dim lower dimensions. Its been implemented in C++ and uses OpenMP (if supported while compiling) for multi-processing; it has thus been sufficiently fast for clustering any larger data sets I have tested so far.

I found some good candidates here ( ), but only the Glass and Iris data-sets have labels for the points. I also found some code to generate Gaussian datasets (SynDECA). The main reason I want this is to compare distance metrics for some clustering methods. It's difficult to use external (extrinsic) evaluation criteria as many of those are biased towards euclidean distances; and there are so many to choose from.

I think you could use any or all. The AccountIdentifier (all unique values) won't help and should probably be left out. I've only used the hierarchical clustering; I think it works well with continuous numeric and ordinal and nominal character data.

This is not an answer, but a related question. I'm hoping someone can comment on this. If one of those variables is something you want to predict, is it appropriate to use that in the clustering? My understanding is that potential explanatory factors might be clustered, but a response variable should not be part of the clustering - then we would subsequently build models to predict the response variable using the clusters as factors (this is assuming that this is a "supervised" analysis with a variable to predict rather than an "unsupervised" analysis to identify patterns without prediction).

To be clear, clustering is a unsupervised technique, meaning that you don't have a response column Y you would like to match/predict. There is no "ground truth" to compare the results of the clustering with. The clustering goal is to find and create homogeneous groups (as best as possible) in a large, diverse and sometimes high-dimensional dataset, with the help of various techniques.

You may have to provide further informations so that we can help you on your topic : dataset, objectives, variables, context... I may actually have more questions than answers, and one of them is related to the goal of your analysis.

Much of Victor has posted is useful. I'm not sure clustering is "unsupervised" (actually not exactly sure what this is?). All data analysis should be a function of what questions you want to answer (what hypotheses you want insight into) and how you got the data. So to the OP, what questions do you want to answer? How did you collect the data you want to use to possibly answer the questions?

Victor, I'm also confused by your statement: "There is no "ground truth" to compare the results of the clustering with." This is true of any analysis. Certainly us mere mortals have no absolute knowledge of the truth. I like Box's quote (in my signature).

@dale_lehman : Agree, if any response is present in the dataset, it should not be included in the clustering (to prevent data/information leakage), and your description looks related to the clustering of variables I'm referring to: use the clusters as inputs in the predictive model.

I do realize the clustering is an "unsupervised" technique - but I believe it is only unsupervised for an initial step. A famous Deming quote: "The only useful function of a statistician is to make predictions, and thus to provide a basis for action." Most of the use cases I can think of for clustering is to eventually use the clusters in some sort of predictive model (the only exception I can think of is when the clusters are used for operations, such as to help create marketing channels or teams). So, for analyses where clusters are to be used for prediction, the issue arises of whether it is meaningful to include the intended response variable when creating the clusters - that is the genesis of my question.

I have a dataset that has 700,000 rows and various variables with mixed data-types: categorical, numeric and binary. I have read several suggestions on how to cluster categorical data but still couldn't find a solution for my problem. The common suggestions are listed below:

1) Use proc distance for the categorical variables to get a distance matrix and then use proc cluster: I can't do that as my dataset is too big to be handled by proc cluster. Another constraint is that even proc fastclus can handle a large dataset but it doesn't work with distance matrix or anything other than numeric data.

3) Another suggestion was to use HPCLUS but HPCLUS can either use only categorical variables or only numeric interval variables to perform clustering but it does not perform clustering for mixed levels of input variables

Have tried using K-mean model on continuous features excluding the categorical feature and using K-mode for categorical feature excluding continuous feature. Kudos to Brian for this excellent post -scally/clustering-categorical-data-in-alteryx/

Dear All, thank you for your time. I have a dataset containing 15,000 sequences. I wish to build a tree and thus my plan was to use BlastClust, a module in the Blast application to cluster them, then use a reference sequence from each cluster to build a crude tree. BlastClust has been running for some time now but I have no idea whether this is going to work or how long it will take.

PS: You might want to have a look at the just published paper Ultrafast clustering algorithms for metagenomic sequence analysis by Li et al. if you're dealing with NGS sequencing data, especially from Metagenomics

This paper presents a clustering method that detects the fiber bundles embedded in any MR-diffusion based tractography dataset. Our method can be seen as a compressing operation, capturing the most meaningful information enclosed in the fiber dataset. For the sake of efficiency, part of the analysis is based on clustering the white matter (WM) voxels rather than the fibers. The resulting regions of interest are used to define subset of fibers that are subdivided further into consistent bundles using a clustering of the fiber extremities. The dataset is reduced from more than one million fiber tracts to about two thousand fiber bundles. Validations are provided using simulated data and a physical phantom. We see our approach as a crucial preprocessing step before further analysis of huge fiber datasets. An important application will be the inference of detailed models of the subdivisions of white matter pathways and the mapping of the main U-fiber bundles. e24fc04721