72 citations · 760 across the 54 of their papers we have counts for
56 papers
Finding coherent node groups in directed graphs
Iiro Kumpulainen, Nikolaj Tatti
Grouping the nodes of a graph into clusters is a standard technique for studying networks. We study a problem where we are given a directed network and are asked to partition the g…
Jaccard-constrained dense subgraph discovery
Chamalee Wickrama Arachchi, Nikolaj Tatti
Finding dense subgraphs is a core problem in graph mining with many applications in diverse domains. At the same time many real-world networks vary over time, that is, the dataset…
Finding Favourite Tuples on Data Streams with Provably Few Comparisons
Guangyi Zhang, Nikolaj Tatti, Aristides Gionis
One of the most fundamental tasks in data science is to assist a user with unknown preferences in finding high-utility tuples within a large database. To accurately elicit the unkn…
Fast likelihood-based change point detection
Nikolaj Tatti
Change point detection plays a fundamental role in many real-world applications, where the goal is to analyze and monitor the behaviour of a data stream. In this paper, we study ch…
Ranking with submodular functions on the fly
Guangyi Zhang, Nikolaj Tatti, Aristides Gionis
Maximizing submodular functions have been studied extensively for a wide range of subset-selection problems. However, much less attention has been given to the role of submodularit…
Dense subgraphs induced by edge labels
Iiro Kumpulainen, Nikolaj Tatti
Finding densely connected groups of nodes in networks is a widely used tool for analysis in graph mining. A popular choice for finding such groups is to find subgraphs with a high…
Recurrent segmentation meets block models in temporal networks
Chamalee Wickrama Arachchi, Nikolaj Tatti
A popular approach to model interactions is to represent them as a network with nodes being the agents and the interactions being the edges. Interactions are often timestamped, whi…
Ranking with submodular functions on a budget
Guangyi Zhang, Nikolaj Tatti, Aristides Gionis
Submodular maximization has been the backbone of many important machine-learning problems, and has applications to viral marketing, diversification, sensor placement, and more. How…
Coresets remembered and items forgotten: submodular maximization with deletions
Guangyi Zhang, Nikolaj Tatti, Aristides Gionis
In recent years we have witnessed an increase on the development of methods for submodular optimization, which have been motivated by the wide applicability of submodular functions…
Approximation algorithms for confidence bands for time series
Nikolaj Tatti
Confidence intervals are a standard technique for analyzing data. When applied to time series, confidence intervals are computed for each time point separately. Alternatively, we c…
Maintaining AUC and -measure over time
Nikolaj Tatti
Measuring the performance of a classifier is a vital task in machine learning. The running time of an algorithm that computes the measure plays a very small role in an offline sett…
Fast computation of distance-generalized cores using sampling
Nikolaj Tatti
Core decomposition is a classic technique for discovering densely connected regions in a graph with large range of applications. Formally, a -core is a maximal subgraph where ea…
Decomposable Families of Itemsets
Nikolaj Tatti, Hannes Heikinheimo
The problem of selecting a small, yet high quality subset of patterns from a larger collection of itemsets has recently attracted lot of research. Here we discuss an approach to th…
Tell Me Something I Don't Know: Randomization Strategies for Iterative Data Mining
Sami Hanhijärvi, Markus Ojala, Niko Vuokko +3
There is a wide variety of data mining methods available, and it is generally useful in exploratory data analysis to use many different methods for the same dataset. This, however,…
Summarizing Data Succinctly with the Most Informative Itemsets
Michael Mampaey, Jilles Vreeken, Nikolaj Tatti
Knowledge discovery from data is an inherently iterative process. That is, what we know about the data greatly determines our expectations, and therefore, what results we would fin…
Maximum Entropy Based Significance of Itemsets
Nikolaj Tatti
We consider the problem of defining the significance of an itemset. We say that the itemset is significant if we are surprised by its frequency when compared to the frequencies of…
Mining Closed Strict Episodes
Nikolaj Tatti, Boris Cule
Discovering patterns in a sequence is an important aspect of data mining. One popular choice of such patterns are episodes, patterns in sequential data describing events that often…
Tiers for peers: a practical algorithm for discovering hierarchy in weighted networks
Nikolaj Tatti
Interactions in many real-world phenomena can be explained by a strong hierarchical structure. Typically, this structure or ranking is not known; instead we only have observed outc…
Mining Closed Episodes with Simultaneous Events
Nikolaj Tatti, Boris Cule
Sequential pattern discovery is a well-studied field in data mining. Episodes are sequential patterns describing events that often occur in the vicinity of each other. Episodes can…
Discovering Episodes with Compact Minimal Windows
Nikolaj Tatti
Discovering the most interesting patterns is the key problem in the field of pattern mining. While ranking or selecting patterns is well-studied for itemsets it is surprisingly und…
Discovering Bands from Graphs
Nikolaj Tatti
Discovering the underlying structure of a given graph is one of the fundamental goals in graph mining. Given a graph, we can often order vertices in a way that neighboring vertices…
Density-friendly Graph Decomposition
Nikolaj Tatti
Decomposing a graph into a hierarchical structure via -core analysis is a standard operation in any modern graph-mining toolkit. -core decomposition is a simple and efficient…
Fast Sequence Segmentation using Log-Linear Models
Nikolaj Tatti
Sequence segmentation is a well-studied problem, where given a sequence of elements, an integer K, and some measure of homogeneity, the task is to split the sequence into K contigu…
Using Background Knowledge to Rank Itemsets
Nikolaj Tatti, Michael Mampaey
Assessing the quality of discovered results is an important open problem in data mining. Such assessment is particularly vital when mining itemsets, since commonly many of the disc…