72 citations · 760 across the 54 of their papers we have counts for
31 papers · 1 filter
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…
Are your Items in Order?
Nikolaj Tatti
Items in many datasets can be arranged to a natural order. Such orders are useful since they can provide new knowledge about the data and may ease further data exploration and visu…
Probably the Best Itemsets
Nikolaj Tatti
One of the main current challenges in itemset mining is to discover a small set of high-quality itemsets. In this paper we propose a new and general approach for measuring the qual…
Discovering Descriptive Tile Trees by Mining Optimal Geometric Subtiles
Nikolaj Tatti, Jilles Vreeken
When analysing binary data, the ease at which one can interpret results is very important. Many existing methods, however, discover either models that are difficult to read, or ret…
The Long and the Short of It: Summarising Event Sequences with Serial Episodes
Nikolaj Tatti, Jilles Vreeken
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. Standard freque…
Significance of Episodes Based on Minimal Windows
Nikolaj Tatti
Discovering episodes, frequent sets of events from a sequence has been an active field in pattern mining. Traditionally, a level-wise approach is used to discover all frequent epis…
Finding Good Itemsets by Packing Data
Nikolaj Tatti, Jilles Vreeken
The problem of selecting small groups of itemsets that represent the data well has recently gained a lot of attention. We approach the problem by searching for the itemsets that co…
Dynamic hierarchies in temporal directed networks
Nikolaj Tatti
The outcome of interactions in many real-world systems can be often explained by a hierarchy between the participants. Discovering hierarchy from a given directed network can be fo…
Inferring the strength of social ties: a community-driven approach
Polina Rozenshtein, Nikolaj Tatti, Aristides Gionis
Online social networks are growing and becoming denser. The social connections of a given person may have very high variability: from close friends and relatives to acquaintances t…
Discovering bursts revisited: guaranteed optimization of the model parameters
Nikolaj Tatti
One of the classic data mining tasks is to discover bursts, time intervals, where events occur at abnormally high rate. In this paper we revisit Kleinberg's seminal work, where bur…
Discovering Nested Communities
Nikolaj Tatti, Aristides Gionis
Finding communities in graphs is one of the most well-studied problems in data mining and social-network analysis. In many real applications, the underlying graph does not have a c…
What is the dimension of your binary data?
Nikolaj Tatti, Taneli Mielikainen, Aristides Gionis +1
Many 0/1 datasets have a very large number of variables; on the other hand, they are sparse and the dependency structure of the variables is simpler than the number of variables wo…
Faster way to agony: Discovering hierarchies in directed graphs
Nikolaj Tatti
Many real-world phenomena exhibit strong hierarchical structure. Consequently, in many real-world directed social networks vertices do not play equal role. Instead, vertices form a…
Distances between Data Sets Based on Summary Statistics
Nikolaj Tatti
The concepts of similarity and distance are crucial in data mining. We consider the problem of defining the distance between two data sets by comparing summary statistics computed…
Safe projections of binary data sets
Nikolaj Tatti
Selectivity estimation of a boolean query based on frequent itemsets can be solved by describing the problem by a linear program. However, the number of variables in the equations…