51. Memetic Differential Evolution Methods for Semi-Supervised Clustering
Invited abstract in session TB-4: Optimization in regression, classification and learning II, stream Optimization in regression, classification and learning.
Thursday, 10:05 - 11:20Room: M:M
Authors (first author is the speaker)
| 1. | Pierluigi Mansueto
|
| Department of Information Engineering, University of Florence | |
| 2. | Fabio Schoen
|
| Dipartimento di Ingegneria dell'Informazione, Università degli Studi di Firenze |
Abstract
In this work, we deal with semi-supervised Minimum Sum-of-Squares Clustering (MSSC) problems where background knowledge is given in the form of instance-level constraints. In particular, we take into account "must-link" and "cannot-link" constraints, each of which indicates if two dataset points should be associated to the same or to a different cluster. The presence of such constraints makes the problem at least as hard as its unsupervised version: it is no more true that each point is associated to its nearest cluster center, thus requiring some modifications in crucial operations, such as the assignment step. In this scenario, we propose novel memetic methodologies based on the Differential Evolution (DE) paradigm and designed to return a (hopefully) optimal solution satysfing all the constraints. The proposals directly extend state-of-the-art DE-based memetic approaches recently proposed for the unsupervised scenario. The new algorithms are compared with some state-of-the-art algorithms from the literature on a set of well-known datasets, highlighting their effectiveness and efficiency in finding good quality clustering solutions.
Keywords
- Mixed integer nonlinear optimization
- Global optimization
- Linear and nonlinear optimization
Status: accepted
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