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
32 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 ...
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
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
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 ...
2
votes
0answers
29 views

Principles to create natural data for a clustering algorithm?

I have come up with an algorithm to cluster geospatial data points. I have quite a few volunteers (100) collecting data for me, using my app on their smart phones to check in at places. However, I'm ...
1
vote
0answers
16 views

Clustering Techniques

I'm a little new to data mining and would definitely appreciate some tips. I'm using clustering algorithms looking for possible grouping in some variables described below. I've been using the Excel ...
0
votes
0answers
29 views

Converting a similarity to a dissimilarity [duplicate]

I am working on a clustering problem for which I have to manually choose the number of clusters. I have a visualization tool that helps me decide whether the clusters are good. In order to ...
-1
votes
1answer
19 views

Converting FuzzykMeans to SphericalFuzzyKMeans?

I grabbed an implementation of FuzzyKMeans (FuzzyCMeans) from the nightly build of the Apache Commons Math library, but I now realize I need to use Cosine Similarity instead of the Euclidean Distance. ...
-1
votes
1answer
45 views

Clustering algorithms assigning probability values

I have a distance matrix for some data I want to cluster. However, I don't just want to assign elements to clusters, but I also want to assign a probability for each element to belong to each cluster. ...
1
vote
0answers
23 views

Regression clustering

I am looking for references about classical methods in regression clustering. My problem is the following: I have a cloud of points that are assumed to have been generated by inverse functions with ...
0
votes
0answers
40 views

Clustering email with mixed types of attribute

I am looking to cluster thousands of emails in one's mailbox. Different from traditional analysis with emphasis on email body, the attachments will play a big role in my work. The data set contains ...
0
votes
0answers
23 views

Tuning the inflation parameter in mcl

I read on the mcl documentation that the inflation parameter can be used to tune the granularity of the clusters. I am not very familiar with graph theory. What is the granularity of the clusters? Can ...
0
votes
3answers
60 views

Converting a distance to a similarity

I am working on a graph clustering algorithm (mcl). It gives the opportunity to give weights to the edges. The weights must be similarities, but I have a distance. The values of this distance range ...
2
votes
0answers
27 views

Choice of an evaluation metric for a graph clustering algorithm

I have instances for which the only thing I know is 70% of the distance matrix. I know some of these points form groups of correlated points (each point of a group is "close" to every point of the ...
2
votes
2answers
139 views

Using the gap statistic to compare algorithms

I want to compare the performances of two clustering algorithms that give me different numbers of clusters. I recently learned about the gap statistic. However, from what I have learned, this ...
0
votes
1answer
39 views

How to define silhouette for one cluster?

I want to compare two clustering algorithms. I took data that the first algorithm gathered in one cluster. The second algorithm gave 3 clusters for the same points. In order to compare the results, I ...
0
votes
3answers
91 views

Scalability of Markov Clustering

I want to do graph clustering on a large dataset (A graph with 600,000 Nodes and tens of millions of edges). I read about Markov clustering. I saw this algorithm involved the calculation of a ...
-2
votes
1answer
42 views

k-medoids algorithm with incomplete distance matrix

I want to apply k-medoids algorithm using an incomplete distance matrix as input. How can I handle the lack of information of this matrix? Just ignoring the missing distances? Or is there a better ...
1
vote
1answer
34 views

k-core clustering algorithm

I am trying to cluster data. Each point in this dataset is connected to some other points. I want to define clusters "depending on how much the points are connected to each other". After some ...
1
vote
0answers
33 views

Random initialization with k-means clustering

I read on my machine learning course (on coursera) that random initialization performed several times and then taking the cluster with the lowest cose could help when the number of clusters is ...
2
votes
2answers
105 views

How to summarize and understand the reults of DBSCAN clustering on big data?

Many clustering algorithms can be used with big data, eg. versions of KMeans, DBSCAN based on Hadoop, etc. But, with k means we will get k centroids for k clusters and we can map them to the space and ...
1
vote
1answer
61 views

Clarification needed about min/sim hashing + LSH

I have a reasonable understanding of the technique to detect similar documents consisting in first computing their minhash signatures (from their shingles, or n-grams), and then use an LSH-based ...
1
vote
0answers
42 views

Difference between BIRCH and Two-Step Clustering

I don't have SPSS. But judging from the documentation you can find on the internet, SPSS "Two-Step Clustering" seems to be closely related to BIRCH clustering: Zhang, T., Ramakrishnan, R., & ...
1
vote
2answers
398 views

Clustering with Weka

Hi everyone and happy new year! I have to analyse a data set with weka clustering, using 3 clustering algorithms and I need to provide a comparison between them about their performance and ...
2
votes
1answer
138 views

Performance metrics to evaluate unsupervised learning

With respect to the unsupervised learning (like clustering), are there any metrics to evaluate performance?
0
votes
1answer
21 views

Could Robust Subspace Clustering be used to remove outliers?

I have two samples set, one is positive, and the other is negative. But, in each of both sets, there are some outliers that don't belong to it. Can the Robust subspace clustering be used to help me ...
1
vote
1answer
91 views

Sweeping across multiple classifiers and choosing the best?

I'm using Weka to perform classification, clustering, and some regression on a few large data sets. I'm currently trying out all the classifiers (decision tree, SVM, naive bayes, etc.). Is there an ...
0
votes
0answers
182 views

Image Clustering using Rapidminer 5

I have a collection of color Images that I want to cluster using Rapidminer 5 and the image processing extension for it. For a start I'd like to cluster them according to the color of the picture but ...
1
vote
1answer
97 views

Clustering of documents that are very different in number of words

I have a corpus of 643 documents with different sizes and my goal is to cluster them according their topics and label each cluster with semantic name for its main topic. I have tired different ...
1
vote
1answer
163 views

Computing document similarity in latent semantic analysis

I have a question regarding Latent Semantic Analysis - after performing SVD decomposition of term-document matrix and choosing some number of dimensions, I get the set of new document vectors. Now, ...
0
votes
0answers
30 views

Fractionation clustering

I have a question regarding fractionation clustering algorithm introduced in http://www.jopedersen.com/Publications/cutting92scattergather.pdf. It's described so vaguely that I'm not sure if I got it ...
0
votes
2answers
57 views

Clustering cloud fitting inverse functions

I have a cloud of points that can be clustered so that each set of points in the cluster can be fitted with an inverse function : $f(x) = cte / x$. What would be an approach for clustering my dataset ...
2
votes
1answer
1k views

LSA vs. PCA (document clustering)

I'm investigation various techniques used in document clustering and I would like to clear some doubts concerning PCA (principal component analysis) and LSA (latent semantic analysis). First thing - ...
-1
votes
1answer
208 views

Normalizing Term Frequency for document clustering

I have a problem understanding the normalization of Term Frequency weight in document Vector Space Model for clustering. Let's say that for document d I have counted occurences of all terms. I ...
4
votes
1answer
974 views

Difference between standard and spherical k-means algorithms

I would like to understand, what is the major implementation difference between standard and spherical k-means clustering algorithms. In each step, k-means computes distances between element vectors ...
1
vote
1answer
106 views

Distances vs. “distance like functions” in clustering

I am studying Kogan's "Introduction to Clustering Large and High Dimensional Data" because I would like to better understand clustering (I never worked with it). Until now "clustering" means to me to ...
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 - ...
1
vote
1answer
63 views

Clustering spatio-temporal data?

I have data in the form of timestamp,lat,long which is gps data for users. I'm new to data mining and want to understand how can I start clustering these data to understand more about it. Should I ...
2
votes
2answers
494 views

Why do we use k-means instead of other algorithms?

I researched about k-means and these are what I got: k-means is one of the simplest algorithm which uses unsupervised learning method to solve known clustering issues. It works really well with large ...
1
vote
0answers
41 views

Using PCA to merge and grade correlated items

I have a real estates' condos sold dataset with the following fields DOM: Date on the market sellPct: Percentage difference between the original and final price. other fields such as Exposure( ...
0
votes
0answers
86 views

How to identify a new pattern in a URL with a machine learning algorithm (Text mining)

I am trying to identify new patterns after analyzing a number of URLs. So let's say, I am investigating the hypothetical website Yoohle.com and their URLs have the following structure. domain = ...
8
votes
3answers
243 views

Detecting clusters in a binary sequence

I have a binary sequence such as 11111011011110101100000000000100101011011111101111100000000000011010100000010000000011101111 Where clusters of mostly 1's are ...
5
votes
1answer
160 views

Binary classification of DNA motif sequences (bioinformatics)

I've been working on on a method for binary classification of DNA sequences. In more detail, here is what the method does. Given a family of DNA sequences, for example DNA sequence motifs, I try to ...
3
votes
1answer
83 views

Spectral clustering of graph

I am trying to cluster the graph using spectral clustering. However I am unaware of the number of classes that exist in the data. Will it be a good idea to do PCA on the adjacency matrix to find ...
2
votes
2answers
557 views

Clustering high-dimensional sparse binary data

I am trying to cluster Facebook users based on their likes. I have two problems: First, since there is no dislike in Facebook all I have is having likes (1) for some items but for the rest of the ...
3
votes
3answers
479 views

Which type of regression fits better?

I am a newbie in data mining world. I have a general question. I have a data set which has 10 independent variables and one target variable named as category which has 9 values like: 1, 2, 3, 4, 5, 6, ...
0
votes
3answers
439 views

Clustering with 3 attributes

Please bear with me because I am very new to data mining. I have a database of 3 attributes: latitude, longitude and temperature. I want to find clusters for the temperature data and I also want to ...
8
votes
5answers
351 views

Does preclustering help to build a better predictive model?

For the task of churn modelling I was considering: Compute k clusters for the data Build k models for each cluster individually. The rationale for that is,that there is nothing to prove, that the ...
1
vote
1answer
448 views

Clustering of time series

I have a set of almost 1600 time series on 2 years which I want to group into clusters. Do you think this is possible using k-means? Which method do you advice me to use? Is this possible at all using ...