16,561 research outputs found
On the Complexity of Mining Itemsets from the Crowd Using Taxonomies
We study the problem of frequent itemset mining in domains where data is not
recorded in a conventional database but only exists in human knowledge. We
provide examples of such scenarios, and present a crowdsourcing model for them.
The model uses the crowd as an oracle to find out whether an itemset is
frequent or not, and relies on a known taxonomy of the item domain to guide the
search for frequent itemsets. In the spirit of data mining with oracles, we
analyze the complexity of this problem in terms of (i) crowd complexity, that
measures the number of crowd questions required to identify the frequent
itemsets; and (ii) computational complexity, that measures the computational
effort required to choose the questions. We provide lower and upper complexity
bounds in terms of the size and structure of the input taxonomy, as well as the
size of a concise description of the output itemsets. We also provide
constructive algorithms that achieve the upper bounds, and consider more
efficient variants for practical situations.Comment: 18 pages, 2 figures. To be published to ICDT'13. Added missing
acknowledgemen
Most Frequent Itemset Optimization
In this paper we are dealing with the frequent itemset mining. We concentrate
on the special case that we only want to identify the most frequent itemset of
length N. To do that, we present a pattern on how to consider this search as an
optimization problem. First, we extract the frequency of all possible
2-item-sets. Then the optimization problem is to find the N objects, for which
the minimal frequency of all containing 2-item-sets is maximal. This
combinatorial optimization problem can be solved by any optimization algorithm.
We will solve them with Quantum Annealing and QUBO with QbSolv by D-Wave. The
advantages of MFIO in comparison to the state-of-the-art-approach are the
enormous reduction of time need, reduction of memory need and the omission of a
threshold. The disadvantage is that there is no guaranty for accuracy of the
result. The evaluation indicates good results
Finding Associations and Computing Similarity via Biased Pair Sampling
This version is ***superseded*** by a full version that can be found at
http://www.itu.dk/people/pagh/papers/mining-jour.pdf, which contains stronger
theoretical results and fixes a mistake in the reporting of experiments.
Abstract: Sampling-based methods have previously been proposed for the
problem of finding interesting associations in data, even for low-support
items. While these methods do not guarantee precise results, they can be vastly
more efficient than approaches that rely on exact counting. However, for many
similarity measures no such methods have been known. In this paper we show how
a wide variety of measures can be supported by a simple biased sampling method.
The method also extends to find high-confidence association rules. We
demonstrate theoretically that our method is superior to exact methods when the
threshold for "interesting similarity/confidence" is above the average pairwise
similarity/confidence, and the average support is not too low. Our method is
particularly good when transactions contain many items. We confirm in
experiments on standard association mining benchmarks that this gives a
significant speedup on real data sets (sometimes much larger than the
theoretical guarantees). Reductions in computation time of over an order of
magnitude, and significant savings in space, are observed.Comment: This is an extended version of a paper that appeared at the IEEE
International Conference on Data Mining, 2009. The conference version is (c)
2009 IEE
Reductions for Frequency-Based Data Mining Problems
Studying the computational complexity of problems is one of the - if not the
- fundamental questions in computer science. Yet, surprisingly little is known
about the computational complexity of many central problems in data mining. In
this paper we study frequency-based problems and propose a new type of
reduction that allows us to compare the complexities of the maximal frequent
pattern mining problems in different domains (e.g. graphs or sequences). Our
results extend those of Kimelfeld and Kolaitis [ACM TODS, 2014] to a broader
range of data mining problems. Our results show that, by allowing constraints
in the pattern space, the complexities of many maximal frequent pattern mining
problems collapse. These problems include maximal frequent subgraphs in
labelled graphs, maximal frequent itemsets, and maximal frequent subsequences
with no repetitions. In addition to theoretical interest, our results might
yield more efficient algorithms for the studied problems.Comment: This is an extended version of a paper of the same title to appear in
the Proceedings of the 17th IEEE International Conference on Data Mining
(ICDM'17
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