Provably-Correct Efficient Algorithms for Clustering

Información del proyecto

PEAC

Identificador del acuerdo de subvención: 748094

DOI

10.3030/748094

Proyecto finalizado el 31 Octubre 2017

Fecha de la firma de la CE 16 Febrero 2017

Fecha de inicio 1 Marzo 2017

Fecha de finalización 28 Febrero 2019

Financiado con arreglo a

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Coste total

€ 200 194,80

Aportación de la UE

€ 200 194,80

Coordinado por

KOBENHAVNS UNIVERSITET
Denmark

Objetivo

Clustering data according to similarity is ubiquitous in computer and data sciences. Similarity between data is often modeled by a distance function: two data points are close if they are similar. This induces a metric space in which each data point is associated to a point of the space. Thus, a clustering according to similarity is a partition of the points such that the distance between two points in the same part is small. Therefore, clustering problems play a crucial role in extracting information from massive datasets in various research areas. However, this problem is hard to formalise: the soundness of a particular clustering often depends on the structure of the data. This induces a gap between theory and practice: on the one hand no guarantee on the practical algorithms can be proven, on the other hand the best theoretical algorithms turn out to be noncompetitive in practice.

By focusing on both the algorithms and inputs that are relevant in practice, the PEAC project aims at rigorously analysing the cutting-edge heuristics and designing more efficient algorithms that are provably-correct for both clustering and hierarchical clustering (HC), bridging a gap between theory and practice.

Very recently, it was shown that a widely-used local search (LS) algorithm achieves the best approximation guarantees for some specific inputs. We plan to design a faster LS-based algorithm for those types of inputs to achieve both better running time and approximation guarantees than the best heuristics. We will design a non-oblivious LS algorithm to obtain a better than the current 2.675 approximation for k-median.

Dasgupta recently introduced a cost function for HC. Using this cost function, we plan to analyse the performances of widely-used heuristics for HC (e.g.: average-linkage, bisection k-means). We will characterize the real-world inputs and use the cost function to design more efficient provably-correct algorithms for HC.

Ámbito científico (EuroSciVoc)

• ciencias naturales › informática y ciencias de la información › ciencia de datos
• ciencias sociales › medios y comunicación › diseño gráfico
• ciencias naturales › informática y ciencias de la información › inteligencia artificial › aprendizaje automático
• ciencias naturales › informática y ciencias de la información › inteligencia artificial › programación heurística

Palabras clave

• Approximation algorithms
• clustering
• hierarchical clustering
• big data
• data structures
• theory of computing

Programa(s)

• H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions PROGRAMA PRINCIPAL
  
• H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
  

Tema(s)

• MSCA-IF-2016 - Individual Fellowships
  

Régimen de financiación

MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)

Convocatoria de propuestas

H2020-MSCA-IF-2016

Coordinador

KOBENHAVNS UNIVERSITET

• Aportación neta de la UEn: € 200 194,80
• Dirección: NORREGADE 10 ; 1165 KOBENHAVN ; Dinamarca
• Región: Danmark › Hovedstaden › Byen København
• Tipo de actividad: Higher or Secondary Education Establishments
• Coste total: € 200 194,80