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Unsupervised and Parameter-Free Clustering of Large Graphs for Knowledge Exploration and Recommendation

Lutov, Artem ; Cudré-Mauroux, Philippe (Dir.)

Thèse de doctorat : Université de Fribourg, 2020 ; no. 2192.

Nous vivons une ère où la quantité d’informations échangées augmente rapidement. Cette quantité croissante de données fait que la capacité de stocker, d’analyser et d’agir sur les informations une préoccupation principale (en plus des problèmes apparents de confidentialité, juridiques et éthiques qui y sont liés), soulevant la question : “Comment peut-on consommer des...

Statix - statistical type inference on linked data

Lutov, Artem ; Roshankish, Soheil ; Khayati, Mourad ; Cudré-Mauroux, Philippe

In: 2018 IEEE International Conference on Big Data (Big Data), 2018, p. 2253–2262

Large knowledge bases typically contain data adhering to various schemas with incomplete and/or noisy type information. This seriously complicates further integration and post-processing efforts, as type information is crucial in correctly handling the data. In this paper, we introduce a novel statistical type inference method, called StaTIX, to effectively infer instance types in Linked Data...

Clubmark: a parallel isolation framework for benchmarking and profiling clustering algorithms on numa architectures

Lutov, Artem ; Khayati, Mourad ; Cudré-Mauroux, Philippe

In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW), 2018, p. 1481–1486

There is a great diversity of clustering and community detection algorithms, which are key components of many data analysis and exploration systems. To the best of our knowledge, however, there does not exist yet any uniform benchmarking framework, which is publicly available and suitable for the parallel benchmarking of diverse clustering algorithms on a wide range of synthetic and...

Accuracy evaluation of overlapping and multi-resolution clustering algorithms on large datasets

Lutov, Artem ; Khayati, Mourad ; Cudré-Mauroux, Philippe