All Questions
Tagged with data-mining clustering
17 questions
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1
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147
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How to detect outliers using DBSCAN?
I am working on a Fraudulent Cash Transaction Detection System using DBSCAN and I want to know what is the proper way to identify outliers?
Thank you
##Edite##
I had a problem how to represent the ...
- 1
1
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1
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918
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Why does the BFR (Bradley, Fayyad and Reina) algorithm assume clusters to be normally distributed around its centroid?
I'm following a course on data mining based on the lectures from Stanford University and the book Mining of massive datasets.
On the topic of clustering, the BFR algorithm is explained with this ...
- 145
2
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0
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85
views
What is the definition of a "Clustering Feature" in BIRCH algorithm?
The paper for BIRCH (a clustering algorithm) contains definitions of a Clustering Feature (CF) where the notation is unclear (cf. PDF page 3 / section 4).
A cluster contains N d-dimensional entries $ ...
- 21
2
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1
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138
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Specific Examples with Explanation of Similarities and Differences of how Distance Functions are used Across Different Fields [closed]
I took a tangent from a student
project
I had done a number of years ago and spent some time studying distance
functions.
(please note that the above link contains the full question with links as I ...
- 21
1
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0
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315
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What is the best stream data clustering algorithm that can handle non-static, uncertain data? [closed]
I have gone through many algorithms including streaming k-means, CluStream etc and they all have their pros and cons. What is the best performing algorithm in terms of
Computational Complexity
...
- 11
2
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0
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26
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Where to cut a category tree
Since I don't have CS background I will most probably ask this question the wrong way. I need to choose a node from a tree, where I include all beneath this node leafs in a validation.
I have a data ...
- 121
2
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1
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287
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Does a distance matrix have to be Euclidean in order to be clustered by an average-linking algorithm (UPGMA)?
What are the exact assumptions behind the use of UPGMA? Can I use a non-Euclidean metric? This may result in a non-Euclidean distance matrix. What kind of bias may I encounter if I do so? References ...
- 21
2
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74
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Clustering with probabilities / vector quantization with arbitrary distance measures
Suppose I'm given $n$ points $x_1,\dots,x_n$ in some space $\mathcal{S}$ (think: $\mathbb{R}^d$), and probabilities $p_1,\dots,p_n$ that form a probability distribution (so $p_1 + \dots + p_n=1$). ...
D.W.♦
- 166k
0
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0
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43
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Adjust FCM algorithm with size constrains
I want to cluster a list of property addressed based on distance and limit the size of each cluster.
The properties are currently assigned to modules by discretion of managers. But I hope to develop ...
user30503
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23
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Is this an accepted/valid clustering evaluation metric?
We have a clustering algorithm where the number of clusters isn't known to the algorithm - it iteratively creates clusters out of similar-looking data points.
The evaluation metric we're currently ...
- 101
0
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89
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Is k-means parallelizable (other than the data parallelism of the distance function computation)
The k-means algorithm is known to be NP-hard.
While the distance function computation in the algorithm loop is data parallel, the algorithm is iterative and may become exponential in the number of ...
- 1,664
2
votes
1
answer
46
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Subspace clustering with random transformation
One approach for clustering a high dimensional dataset is to use linear transformation, and the most common approaches are PCA and random projection (where random projection arises from the Johnson-...
- 51
3
votes
0
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91
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Document clustering for summarization
I am curious as to what steps one would reasonably need to take to perform an extraction-based text summarizer.
I've taken a look at some papers I've found on Google such as this one, which explains ...
- 131
1
vote
1
answer
755
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Is there an efficient way to cluster a graph according to Jaccard similarity?
Is there an efficient way to cluster nodes in a graph using Jaccard similarity such that each cluster has at least k nodes?
Jaccard similarity between nodes i and j: Let S be the set of neighbours ...
- 151
3
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1
answer
135
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Analysis and classification based on data points
I'm not sure if this is the correct stack exchange or correct tags, but my question is as follows:
I am working on a sort-of ratings system for players in a particular game. After allowing the ...
- 153
1
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0
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71
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How to cluster nodes based on the number of dependencies
I have a problem where, there are a set of nodes and dependencies between them. I want to cluster them based on the maximum number of dependencies. Dependencies can be thought of as number of edges ...
- 2,221
30
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4
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71k
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What exactly is the difference between supervised and unsupervised learning?
I am trying to understand clustering methods.
What I I think I understood:
In supervised learning, the categories/labels data is assigned to are known before computation. So, the labels, classes or ...
- 403