2 citations · 4 across the 4 of their papers we have counts for
4 papers
Formalizing Interruptible Algorithms for Human over-the-loop Analytics
Austin Graham, Yan Liang, Le Gruenwald +1
Traditional data mining algorithms are exceptional at seeing patterns in data that humans cannot, but are often confused by details that are obvious to the organic eye. Algorithms…
A comparison study of object-oriented database clustering techniques
Jérôme Darmont, Le Gruenwald
It is widely acknowledged that a good object clustering is critical to the performance of OODBs. Clustering means storing related objects close together on secondary storage so tha…
Frequent itemsets mining for database auto-administration
Kamel Aouiche, Jérôme Darmont, Le Gruenwald
With the wide development of databases in general and data warehouses in particular, it is important to reduce the tasks that a database administrator must perform manually. The ai…
Dynamic Clustering in Object-Oriented Databases: An Advocacy for Simplicity
Jérôme Darmont, Christophe Fromantin, Stéphane Régnier +2
We present in this paper three dynamic clustering techniques for Object-Oriented Databases (OODBs). The first two, Dynamic, Statistical & Tunable Clustering (DSTC) and StatClust, e…