Cluster analysis is the task of 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.]

learn more… | top users | synonyms (1)

0
votes
0answers
4 views

Replying the canonical discriminant analysis on the test set

I run a canonical discriminant analysis following those 2 video lessons on Coursera. Now, I would like to test the accuracy of the clustering methodology on a test set by replicating the canonical ...
0
votes
1answer
14 views

Similarity measure for clusterings in graphs evolving over time (temporal network)

From what I understand about clusters, they can be obtained from an existing graph at 1 instance of time. But consider the situation of a temporal network, such as a social network, where the graph ...
1
vote
2answers
494 views

Gaussian neighborhood function and non linear learning rate for self-organizing map in R

I've been working on SOMs and how to get the best clustering results. One approach could be to try many runs and choose the clustering with the lowest within sum of squared errors. However, I do not ...
3
votes
0answers
57 views

Was it as valid to perform k-means on a distance matrix as on data matrix (text mining data)?

(This post is a repost of a question I posted yesterday (now deleted), but I've tried to scale back volume of words and simplify what I'm asking) I'm hoping to get some help interpreting a kmeans ...
0
votes
1answer
28 views

normalisation in k means clustering on percentages and other numerical variables

I have several variables to include in k-means, some of them are percentages (between 0-1) and some of them are numerical variables (positive values). I know normalisation is required when the ...
5
votes
5answers
127 views

What is the best way to detect repetition in xyz data for purposes of splitting data?

I'll use this picture to explain What I want to do is define some patterns as trained patterns. Then given data I want to be able to determine if the pattern exists in the dataset, and if it does ...
0
votes
0answers
13 views

clustering based on string variables?

I am working on a project and currently experimenting cluster analysis. The dataset is mainly string variables, and I think my biggest three challenges in the data cleaning phase are the following: ...
0
votes
0answers
9 views

analysis of nationwide inpatient sample data [on hold]

Has anyone analyzed NIS data using R or SPSS? Is there code available for this analysis? I have a dataset ranging from 2003 until 2009 and am comparing three surgical groups. I am not sure how to ...
3
votes
1answer
383 views

Quantitative results of cluster analysis

Currently, I am doing a clustering 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. For the ...
1
vote
1answer
28 views

What is the definition of a kernel on vertices or edges?

I am currently trying to perform clustering on a collection C of undirected and unlabeled graphs. I decided to use to a kernel on graphs to obtain the kernel matrix of C. Then I can derive the ...
0
votes
0answers
15 views

What is the best way to cluster the gower similarity matrix?

I have a dataset that contains the gower similarity of each observation from each other. So the dataset looks like this: ...
3
votes
1answer
127 views

Choosing the right linkage method for hierarchical clustering

I am performing hierarchical clustering on data I've gathered and processed from the reddit data dump on Google BigQuery. My process is the following: Get the latest 1000 posts in /r/politics ...
4
votes
1answer
431 views

The best way for clustering an adjacency matrix

I've had a hard time interpreting resulting clusters of an adjacency matrix. I have 200 relatively big matrices representing subjects that contains partial correlations (z scores) of time series (...
0
votes
1answer
21 views

Clustering sets of vectors

I have a set of $d$-dimensional vectors $\{v_1,v_2,\dots,v_n\}$, each of which has been assigned a label from a set $S=\{s_1,s_2,\dots,s_k\}$. I would like to find another set of labels $T=\{t_1,t_2,\...
6
votes
3answers
327 views

How to select or validate the selection of a clustering method?

One of the biggest issue with cluster analysis is that we may happen to have to derive different conclusion when base on different clustering methods used (including different linkage methods in ...
1
vote
1answer
97 views

Spatial cluster analysis

Let's say I have a structure like this : This is a spatial region with measurement of plant population in each site. Black and red represent two regions with different intensities.The question is ...
2
votes
1answer
189 views

Incorporating new words in tfidf feature-vector for online clustering

I am building an Online news clustering system using Lucene and Mahout libraries in java. I intend to use vector space model and tfidf weights for Kmeans(or fuzzy/streamKmeans). My plan is : Cluster ...
0
votes
0answers
21 views

How to measure clustering algorithm performance? [duplicate]

For supervised learning, both regression and classification have ground truth. The model performance can be measured against ground truth. For example, $R^2$ in regression or accuracy (0-1) in ...
2
votes
0answers
32 views

Choosing evaluation measure for non-parametric clustering

I have to cluster some data using non-parametric clustering technique which is given in this paper. After all the cluster evaluation measure used in this paper is Normalized Mutual Information as they ...
0
votes
0answers
8 views

LSA Clusters Have the Same Words

I use LSA to create 5 document clusters and identify top 10 words nearest to cluster centroid. I found 4 words appear in all clusters(good, stay, staff, clean) . How to deal with this outcome? Should ...
2
votes
1answer
15 views

How do I choose the appropriate numbers of customers to be considered for cluster analysis?

I am currently doing a customer segmentation project in SAS. I have identified 2700 customers who are have made a purchase in each of the 4 years I am analysing. For the cluster analysis the more ...
0
votes
0answers
17 views

how to measure clustering task with unlabelled data set [duplicate]

I wanna know, how to measure the accuracy of a clustering method when we deal with data set without an a priori knowledge about class belonging ? (the data used for the clustering task, do not contain ...
3
votes
2answers
2k views

How to determine which method is the most valid, reasonable clustering results?

Method 1: Cluster by K-means with initial centroid {27, 67.5} Method 2: Cluster by K-means with initial centroid {22.5, 60} Method 3: Agglomerative Clustering How can I know which method gives a ...
3
votes
1answer
1k views

Clustering text with python

I have asked on StackOverflow, but they suggested me to move here for better answers. I copy paste the question. I have decided to play a little with similarities and clustering text. I have already ...
2
votes
1answer
128 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 ...
9
votes
4answers
279 views

With categorical data, can there be clusters without the variables being related?

When trying to explain cluster analyses, it is common for people to misunderstand the process as being related to whether the variables are correlated. One way to get people past that confusion is a ...
2
votes
2answers
160 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 ...
4
votes
4answers
117 views

Can PCA allow to identify redundant variables that can be removed before doing cluster analysis?

I hope this is suitable for this forum: I am new to PCA and what I ultimately want to do is perform cluster analysis on my dataset. I have 20 physical descriptor variables for organisms, each with ...
0
votes
0answers
3 views

Characterize clusters composition

The problem I am performing a two-steps clustering task on a large dataset (a dozen variables but several hundred thousands of observations) by first using k-means and then using a Hierarchical ...
-1
votes
0answers
34 views

Clustering in Industry - Why only k-means? [closed]

How successful is clustering in industry as opposed to academics? More specifically, it seems like clustering algorithms geared towards mixed data type data sets aren't implemented by the common ...
8
votes
1answer
3k views

Jenks Natural Breaks in Python: How to find the optimum number of breaks?

I found this Python implementation of the Jenks Natural Breaks algorithm and I could make it run on my Windows 7 machine. It is pretty fast and it finds the breaks in few time, considering the size of ...
12
votes
4answers
2k views

Does “curse of dimensionality” really exist in real data?

I understand what is "curse of dimensionality", and I have done some high dimensional optimization problems and know the challenge of the exponential possibilities. However, I doubt if the "curse of ...
0
votes
1answer
171 views

Random forest clustering

In my data the classes were defined by binning a variable in 10 bins. After growing the random forest its proximity matrix is viewed as the following MDSplot: As can be seen from the plot all ...
8
votes
3answers
7k views

Cluster Big Data in R and Is Sampling Relevant?

I'm new to data science and have a problem finding clusters in a data set with 200,000 rows and 50 columns in R. Since the data have both numeric and nominal variables, methods like K-means which ...
0
votes
0answers
4 views

Hard partitioning of the association matrix

I obtain a co-association matrix $n \times n$ that corresponds to the maximum likelihood estimate of the probability of pairs of variables being in the same cluster. Further suppose that there are $k$ ...
0
votes
0answers
11 views

Moving Data From Scikit-Learn to Elki for Clustering [migrated]

I have 100,000 sentences that I've processed into TF-IDF vectors using scikit-learn's TfidfVectorizer with highly customized stopwords and nlp stemming. My goal is ...
-1
votes
0answers
6 views

Does anyone have a good implementation of ENCLUS subspace clustering? [closed]

I am looking for an implementation (preferable Matlab) of the entropy-based subspace clustering ENCLUS. Instead of starting coding it, I would very much like to have one working. Thanks for any help!!
-1
votes
0answers
29 views

Clustering of variables with mixed type [duplicate]

I have a dataset with 6 variables. Some variables are continuous numeric while others are discrete. The data has the following structure: ...
-1
votes
0answers
22 views

cluster external validation [closed]

I am using ELKI in order to perform location clustering with DBSCAN and OPTICS. My data set include 30 participants but it is not labeled but I do have pair of coordinates (e.g. home, work, etc) as ...
1
vote
4answers
106 views

Deterministic clustering approaches

I need a deterministic [in the sense - robust to the ways of initial input / initial seeds] clustering method to group values in distributions that could be either random, normal or log-normal. ...
0
votes
1answer
152 views

Alternative of canopy clustering algorithm in K-means algorithm

I am analyzing implementation of K-means clustering algorithm in MadLib project. Here K-means algorithm uses Canopy clustering for initial set of Centroid.I am just wondering , are there any other ...
9
votes
1answer
2k views

Clustering probability distributions - methods & metrics?

I have some data points, each containing 5 vectors of agglomerated discrete results, each vector's results generated by a different distribution, (the specific kind of which I am not sure, my best ...
3
votes
1answer
233 views

In non-negative matrix tri-factorization, initialization not possible because matrix is singular

I have implemented the non-negative matrix tri-factorization algorithm (link to paper). If is similar to the more widely known NMF (non-negative matrix factorization), but incorporates prior knowledge ...
2
votes
1answer
196 views

How to consider different samples in functional data clustering?

In the engineering context several data sources like different kinds of measurement signals (for example distances, angles and efficiencies) are very common. If it would be possible to observe these ...
5
votes
5answers
217 views

Clustering of variables: but they are mixed type, some are numeric, some are categorical

I have a dataset with 15 variables. Some variables are numeric, continuous. Other variables are boolean, dichotomous (true/false). There's also one variable categorical, nominal. ...
1
vote
1answer
50 views

Instruct me about K-Means Clustering

I have been instructed by my supervisor to run K-means in Matlab on my data which is comprised of sensory data observations that pertain to 7 outcomes, which I have labeled using numbers from 0 to 7. ...
1
vote
1answer
151 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 ...