Questions tagged [data-mining]
The data-mining tag has no usage guidance.
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Boosting the probability of success(random projections, johnson lindenstrauss)
In the simple proof of the johnson lindenstrauss lemma written by Sanjoy Dasgupta, Anupam Gupta that can be found here they state the following (p.$62$):
Repeating this projection $O(n)$ times can ...
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Has there already been research done on how data(e.g. runtime) can improve the development environment of a language?
tl;dr; I am being offered a graduate thesis about how to use data about a languages runtime/static analysis of dependencies etc. and feed it back into the development process. And my question is: Has ...
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Pagerank in directed *acyclic* graphs (DAG)
I deal with pagerank computations on large directed acyclic graphs (DAG).
I found no reference to work on this specific case, only some work on pagerank in more specific cases, e.g., PageRank of Scale ...
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What is the meaning of an Oracle in data clustering?
I am not sure whether this is the best place to ask this question. I am in the process of researching the area in data clustering as well as the algorithms that are associated with it and the term ...
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VC dimension in data mining
Can someone please explain to me (with normal language) - how is VC dimension related to data mining (frequent itemset mining - PAC learning). (incl. how we define range space as it's written in ...
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Upper bound on the size of a Concept Lattice (Galois Lattice)?
A context is a tuple $(O, A, R)$ where $O$ is the set of objects, $A$ the set of attributes and $R \subseteq O\times A$ is a relation. For $o \in O$ and $a \in A$ we read $oRa$ as the object $o$ ...
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Upper bound to number of closed itemsets
Given a set $I$ of $n$ items, and a collection $D$ of $m<2^n$ subsets of $I$, a closed itemset is a subset $A$ of $I$ that is contained in strictly more elements of $D$ than any of its proper ...
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Analyze tags similar tags
I have a system for tagging articles. Each editor ads different tags to each piece of content. For example:
for A: George Washington, first president
for B: first president, Washington George
for C: ...
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Locality Sensitive Hashing - meaning of a block
I'm reading one of the early LSH papers and I'm a little confused by the meaning of a "block". In particular, in the proof of theorem 1 in section 3.2 (p 522), what are the blocks being pointed to? ...
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Self-organizing maps algorithm(Kohonen networks)
In self-organizing maps(SOM) algorithm described here it is said about the weights of nodes and data items. If using this algorithm for sine function approximation given a few points are the nodes - ...
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Finding most informative feature subsets given dataset, clustering algorithm and gold standard partition
I have an $n \times m$ matrix of data $\mathbf{D}$ as well as a $k$-partition $P$ of $n$ indices each representing a row in a dataset. Assuming an arbitrary clustering algorithm $A$, I would like to ...
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Techniques to get nodes in the best Markov Cluster?
I was using Markov Clustering to cluster nodes in my bidirectional graph, and overall the results were great. However, there were a couple instances where a weakly connected node would attract a node ...
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Is there a way to detect search engine bias?
Search engines are increasingly being relied on as information gatekeepers, yet the criteria used by search engines to rank results is opaque to users. How can users be sure their results aren't ...
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Fuzzy K-modes clustering how to find the cluster centers
I'm trying to understand [fuzzy k-modes][1] algorithm (look mainly at page 3) in order to implement it.
I'm stuck at the calculation of cluster centers they said as shown in the link
https://...
- 101
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A better way to cluster items
I am working on a text processer which gives out similarities between a set of strings. After weighted LCS, Levenshtein distance and double metaphone matching, I get buckets of strings such as
...
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Help needed on method to use for anomaly detection [closed]
I think people here could guide me in solving a problem related to anomaly detection. The term anomaly here refers to some malware attack.
I could get information about the malware infection from ...
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K-NN or matrix factorization for discovering correlated features?
I am looking to cluster users together in a database, with each user represented by a number of features that are both discrete and continuous in nature. "Similar" users should be clustered together ...
DanB