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Found 24 papers, 2 papers with code
Fast likelihood-based change point detection
We then show that for fixed bernoulli parameters we can find the optimal change point in logarithmic time.
Recurrent segmentation meets block models in temporal networks
We extend this model to temporal networks by modelling the edges with a Poisson process.
Ranking with submodular functions on a budget
In more detail, given a set of items and a set of non-decreasing submodular functions, where each function is associated with a budget, we aim to find a ranking of the set of items that maximizes the sum of values achieved by all functions under the budget constraints.
Maintaining AUC and $H$-measure over time
The first algorithm maintains area under the ROC curve (AUC) under addition and deletion of data points in $O(\log n)$ time.
Approximation algorithms for confidence bands for time series
If we have a constraint $k$ for which we cannot find appropriate $\alpha$, we demonstrate a simple algorithm that yields $O(\sqrt{n})$ approximation guarantee by connecting the problem to a minimum $k$-union problem.
Decomposable Families of Itemsets
Such itemset families define a probabilistic model for the data from which the original collection of itemsets has been derived from.
Tell Me Something I Don't Know: Randomization Strategies for Iterative Data Mining
At each step in the data mining process, the randomization produces random samples from the set of data matrices satisfying the already discovered patterns or models.
Summarizing Data Succinctly with the Most Informative Itemsets
As we use the Maximum Entropy principle to obtain unbiased probabilistic models, and only include those itemsets that are most informative with regard to the current model, the summaries we construct are guaranteed to be both descriptive and non-redundant.
Maximum Entropy Based Significance of Itemsets
We say that the itemset is significant if we are surprised by its frequency when compared to the frequencies of its sub-itemsets.
Mining Closed Episodes with Simultaneous Events
Episodes are sequential patterns describing events that often occur in the vicinity of each other.
Discovering Episodes with Compact Minimal Windows
Discovering the most interesting patterns is the key problem in the field of pattern mining.
Mining Closed Strict Episodes
Adopting existing approaches for discovering traditional patterns, such as closed itemsets, to episodes is not straightforward.
Discovering Bands from Graphs
To this end, we formulate an optimization problem: given a graph and an integer $K$, we want to order graph vertices and partition the ordered adjacency matrix into $K$ bands such that bands closer to the diagonal are more dense.
Finding Robust Itemsets Under Subsampling
We do this by measuring the robustness of a property of an itemset such as closedness or non-derivability.
Comparing Apples and Oranges: Measuring Differences between Exploratory Data Mining Results
Our approach provides a means to study and tell differences between results of different exploratory data mining methods.
Using Background Knowledge to Rank Itemsets
We demonstrate that these statistics describe forms of data that occur in practice and have been studied in data mining.
The Long and the Short of It: Summarising Event Sequences with Serial Episodes
An ideal outcome of pattern mining is a small set of informative patterns, containing no redundancy or noise, that identifies the key structure of the data at hand.
What is the dimension of your binary data?
Defining the effective dimensionality of such a dataset is a nontrivial problem.
Efficient estimation of AUC in a sliding window
More specifically, we propose an algorithm that, given $\epsilon$, estimates AUC within $\epsilon / 2$, and can maintain this estimate in $O((\log k) / \epsilon)$ time, per update, as the window slides.
Boolean matrix factorization meets consecutive ones property
In this paper, we study a problem of Boolean matrix factorization where we additionally require that the factor matrices have consecutive ones property (OBMF).
Strongly polynomial efficient approximation scheme for segmentation
In addition, we consider a cumulative version of Seg, where we are asked to discover the optimal segmentation for each prefix of the input sequence.
Balancing information exposure in social networks
Our goal is to find two sets of nodes to employ in the respective campaigns, so that the overall information exposure for the two campaigns is balanced.
A note on adjusting $R^2$ for using with cross-validation
We show how to adjust the coefficient of determination ($R^2$) when used for measuring predictive accuracy via leave-one-out cross-validation.
Skopus: Mining top-k sequential patterns under leverage
It combines (1) a novel definition of the expected support for a sequential pattern - a concept on which most interestingness measures directly rely - with (2) SkOPUS: a new branch-and-bound algorithm for the exact discovery of top-k sequential patterns under a given measure of interest.
