Partitioning data into subsets of objects according to their mutual "similarity," without using preexisting knowledge such as class labels. Clustered-standard-errors and/or cluster-samples should be tagged as such; do not use the "clustering" tag for them.

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0
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2answers
27 views

How do we analyse likelihood in a dataset?

I am working to analyze poverty rate using census data. I have a huge dataset. I want to extract the likelihood from this dataset in order to create patterns for energy consumption. Let's say this: ...
-2
votes
3answers
33 views

Agglomerative Hierarchical Clustering “complete linkage” as opposed to “single linkage” dendrogram

Will any dataset clustered via each of the following methods: Agglomerative Hierarchical Clustering using "complete linkage" method Agglomerative Hierarchical Clustering using "single linkage" ...
-1
votes
1answer
41 views

k-Means Clustering vs. Hierarchical Clustering

Can you please provide One advantage of "k-Means" compared to "Hierarchical Clustering" One advantage of "Hierarchical Clustering" compared to "k-Means" Thanks in advance !!
5
votes
1answer
197 views

Spatial clustering with the constraint that all clusters have equal count

I wish to perform a spatial clustering of scattered data that represents geographic locations of individuals in an urban area. Hierarchical clustering seems to work well, and I have successfully done ...
-1
votes
1answer
31 views

Cluster with distance threshold in R

I'd like to get clusters with a maximum inner distance threshold. Now I use hc <- hclust(d) and cutree(hc, numofclasses). ...
0
votes
1answer
15 views

clustering of singular values

let us consider following graph of singular values i want to make some kind of clustering of these data,namely to seperate main components from non main components,let say signal components ...
1
vote
2answers
25 views

How to perform K-medoids when having the distance matrix

I've been trying for a long time to figure out how to perform (on paper)the K-medoids algorithm, however I'm not able to understand how to begin and iterate. for example: I have the distance matrix ...
0
votes
1answer
73 views

Three-dimensional phylogenetic tree “anchored” in a scatter plot

I have done a simple clustering (protoclust) using error-containing data. To determine distances, I used a simple "pseudo-d" distance, in which the absolute value of the difference between two points ...
0
votes
0answers
14 views

Feature Selection for look alike modeling using k-NN

I have a list of items(around 7000) and various parameters for each items. For each item on my list i need to identify 10 items which are similar to the item from my whole population (17 million). I ...
0
votes
0answers
34 views

How to compare two different clustering approaches?

I'm making a project connected with identifying the dynamics of sales. My database concerns 26 weeks (so equally in 26 time-series observations) after launching the product, 126 time-series=126 ...
1
vote
1answer
74 views

Rand index calculation

I'm trying to figure out how to calculate the Rand Index of a cluster algorithm, but I'm stuck at the point how to calculate the true and false negatives. At the moment I'm using the example from the ...
0
votes
0answers
14 views

Complete Linkage Clustering of 3D data space coordinates

I have a large dataset of 3d points (XYZ coordinates) and I would like to perform hierarchical clustering using complete linkage method with Euclidean distance as clustering metric. Additionally, ...
1
vote
1answer
28 views

Algorithm for scoring co-varying traits

I am sure this has been done, but I can't find quite the right approach. EDIT: Trying to explain better. The rows of colored boxes below are columns of molecular sequence data -- positions in a ...
0
votes
1answer
33 views

Performing hierarchical clustering on a large data set

I have been applying complete linkage on about 5,000 points using matlab with no problem. I want to extend this method to much more elements. It would take me a long time to process my data to test ...
2
votes
1answer
31 views

How can I evaluate the performance of a system that generates word clusters?

The word2vec tool uses deep learning to compute vector representations of words. They've mentioned that - "The word vectors can be also used for deriving word classes from huge data sets. This is ...
0
votes
1answer
179 views

F-measure for document clustering evaluation - NaN

I'm developing the Java application for text document clustering, and I'm researching some evaluation methods. I implemented F-measure (http://en.wikipedia.org/wiki/F1_score), but I have a problem - ...
2
votes
0answers
27 views

Which steps have to be done before fitting logistic curve to time-series?

I want to cluster time-series concerning sales of products. In the database I have 26weeks after launching each products and units sold each week. One of the method of clustering is to cluster ...
0
votes
1answer
87 views

Quantitative results of clustering analysis

Currently, I am doing a clustering analysis for two sets of data. One smaller dataset (about 100 data) got ground truth labels, and one larger dataset (about 2000 data) has no ground truth labels. ...
5
votes
1answer
203 views

Memory requirements of $k$-means clustering

Can anyone tell me the factors that affect the memory requirements of $k$-means clustering with a bit of explanation?
25
votes
5answers
4k views

How to tell if data is “clustered” enough for clustering algorithms to produce meaningful results?

How would you know if your (high dimensional) data exhibits enough clustering so that results from kmeans or other clustering algorithm is actually meaningful? For k-means algorithm in particular, ...
0
votes
1answer
58 views

usefulness of k-means clustering on high dimensional data [duplicate]

I wonder what is the usefulness of k-means clustering in high dimensional spaces, and why it can be better (or not) than other clustering methods when dealing with high dimensional spaces.
0
votes
0answers
9 views

How to Hybrid the clustering and classification model [closed]

Hi i am working on classification . By reading some papers on classification i found that the result of hybrid of clustering and classification provides better result. But, i donot know how to hybrid ...
2
votes
2answers
101 views

Relevance of overall absolute values in covariance analysis of two variables

I am performing K means clustering on a gene expression dataset. I am aware of the fact that the Pearson correlation metric allows to group trends or patterns irrespective of their overall level of ...
0
votes
1answer
100 views

Document image analysis and retrieval with online incremental clustering

Is there any interesting problem in the area of "Document Image Analysis and Retrieval" which by nature needs an online/incremental clustering process ? The problem may be in the context of "Logical ...
0
votes
1answer
20 views

Silhouette scores for different distance metrics

I clustered a data set using PAM with a euclidean distance metric and a pearson correlation distance metric. The average silhouette value of the correlation clusters is higher at most points than the ...
0
votes
1answer
44 views

Which clustering algorithm shall I use?

I need some help My project aims to develop algorithms for spatial temporal analysis of Flickr, Twitter and Foursquare databases to detect any kind of significant changes, named as “Event” in real ...
1
vote
1answer
46 views

Finding the best dataset for classification

I have 100 datasets. All of them have varying number of features. There are around 20,000 samples in each of them. Every $i$-th sample in the 100 datasets has the same label ($0/1$). The data is ...
2
votes
1answer
84 views

Distance between two Gaussian mixtures to evaluate cluster solutions

I'm running a quick simulation to compare different clustering methods, and currently hit a snag trying to evaluate the cluster solutions. I know of various validation metrics (many found in ...
1
vote
0answers
48 views

Variance Inflation Factor to Address Spatial Grouping with Binary Dependent Variable

I want to obtain reliable standard errors of the estimated coefficients from a regression of y on x. The observation for each individual consisted of a value of the y variable, which is binary, and a ...
0
votes
2answers
33 views

Streaming k-means

I want to perform something like streaming/online/out-of-core kmeans clustering on large data. Here is simple idea: Break all data into N chunks. Read from disk 1st chunk and calculate centroids ...
0
votes
1answer
21 views

Evaluation indexes hypothesis for clustering

I read on the cluster analysis page of wikipedia: For example, k-means clustering can only find convex clusters, and many evaluation indexes assume convex clusters. On a data set with non-convex ...
1
vote
1answer
53 views

cluster plot: working and interpretation ?

Recently I have come across usage of cluster plot, which combines k-mean clustering along with PCA. The plot shows different clusters plotted using first two PCs. I have checked some of the threads ...
1
vote
1answer
36 views

How does the Bayes' theorem equation generalize all sorts of regression/classification models?

I have been reading “Pattern Recognition & Machine Learning” written by Christopher M. Bishop for some time, but I am still a beginner. I wish to get a bigger view that summarizes regression and ...
2
votes
0answers
30 views

Cluster analysis on related factors

I am analyzing a public data set of information security incident data and trying to find "clusters" of related factors. Specifically, each incident is analyzed using VERIS for the actor's variety ...
2
votes
1answer
56 views

Detecting strong currents in a sparse directed graph

I have a very large, sparse, weighted, directed graph. The structure is such that it mainly consists of strings of nodes connected with highly weighted edges. These strings can be connected by weak ...
1
vote
0answers
40 views

Clustering time series of measurements in R

I have a dataframe consisting time series of measurements taken every hour for 366 days or a year. Below is shown a sample of hourly measurements for the first two weeks. I want to cluster days with ...
0
votes
0answers
10 views

k-way MinMax spectral clustering

Is there a k-way MinMax Cut Spectral Clustering which can be easily implemented? In the spectral clustering tutorial I found only 2-way MinMax cut.
1
vote
0answers
21 views

Finding multivariate clusters with survey data (in R)

I'd like to conduct a multivariate cluster analysis on data from the American Community Survey's PUMS microsample (individual level records). I've only performed cluster analysis before when there are ...
0
votes
0answers
27 views

What is the best algorithm for finding attacks from log file [closed]

I m working on forensic analysis of web logs. I have generated the DoS attack dataset and i m having the attack dataset of log files (unlabeled dataset) taken from Dr. Anton Chuvakin. I need to look ...
4
votes
2answers
91 views

What's the easiest way to separate two populations in a scatterplot?

I have to separate two populations by a line in a scatterplot: I would like find a threshold that separates the two populations. In @Waynes words, I would like to cluster the points into two ...
0
votes
1answer
112 views

Multidimensional scaling using Python

I have 6,000 points for which I have all pairwise distances in a distance matrix. I want to get an idea whether these data were generated by a mixture of Gaussian distributions so I'm trying to get a ...
2
votes
2answers
109 views

Special method for forecasting on time-series clusters in R?

I'm doing a project related to identifying sales dynamics. My database contains 26 weeks after launching the product (so 26 time-series observations equally spaced in time). I used two methods ...
1
vote
2answers
125 views

Similarity between objects based on tags (binary features)

I have five millions of objects each of them having one or more tags. How do I compute statistically sound similarity score between each pair of the objects taking into account that: There are 100 ...
1
vote
1answer
34 views

Alternative to spherical K-Means for clustering large high dimensional dataset

What are some alternatives to Spherical K-Means for clustering very large datasets of high dimension? I'm looking for something that will be fast even on large datasets, and preferably will not ...
0
votes
0answers
18 views

processing a sound file for analysis of different spoken languages

So i have sound files for 5 languages by 2 person, thus my input data has 10 sound files. Now i want to cluster them based on the languages (thus 5 clusters) and not based on the speaker/voice ( ...
1
vote
0answers
19 views

Comparison of cophenetic correlation coefficients on different data sets

I applied the same hierarchical clustering (weighted) on two data sets: The first is a 'raw' data set, on which I didn't do anything, and the second on the same data set after I filtered it by ...
0
votes
0answers
21 views

combining two categorical variables

I have one five point Likert scale variable (importance levels) for accessibility to a certain facility, and another three-level categorical variable (preferred distance). I want to combine these ...
0
votes
0answers
28 views

statistical significance test on cluster analysis result

I've done a hierarchical clustering on a data-set composed by 33 subjects and 2 continuous variables (called V1 and V2), which produces 3 clusters. Now I'm wodering if it make any sense to perform a ...
2
votes
0answers
25 views

What is a good technique for grouping objects based on binary or dichotomous traits?

I have a set of objects each of which has a list of traits. Data on the traits is binary: an object has a trait or does not. The number of objects that I have is moderately greater than the number ...
0
votes
0answers
23 views

Converting a spearman correlation to a euclidian dissimilarity

I am applying ward hierarchical clustering on a data set for which I have pairwise similarities. Since hierarchical clustering need a dissimilarity matrix, I am trying to convert my similarity matrix ...