2022 Developer Survey is open! Take survey.

Questions tagged [data-mining]

The tag has no usage guidance.

Filter by
Sorted by
Tagged with
4 votes
1 answer
359 views

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 ...
user avatar
0 votes
0 answers
45 views

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 ...
user avatar
  • 11
0 votes
1 answer
163 views

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 ...
user avatar
2 votes
1 answer
119 views

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 $o$ ...
user avatar
  • 427
2 votes
1 answer
113 views

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 ...
user avatar
  • 569
0 votes
0 answers
27 views

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: ...
user avatar
  • 101
2 votes
0 answers
162 views

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? ...
user avatar
  • 21
0 votes
1 answer
330 views

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 - ...
user avatar
  • 109
3 votes
0 answers
122 views

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 ...
user avatar
1 vote
0 answers
111 views

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 ...
user avatar
  • 111
9 votes
1 answer
221 views

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 ...
user avatar
  • 99
0 votes
0 answers
298 views

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://...
user avatar
  • 101
0 votes
0 answers
149 views

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 ...
user avatar
  • 523
0 votes
1 answer
253 views

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 ...
user avatar
  • 709
2 votes
1 answer
468 views

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 ...
user avatar