Hierarchical Affinity Propagation
نویسندگان
چکیده
Facility location (FL) is a well-researched problem in operational-research (Şahin Güvenç & Haldun, 2007), and is closely related to EBC. Given a set of N customers and M potential facilities to open, as well as costs sij for customer i to use facility j, and a facility setup cost cj associated with opening a facility j, we wish to find the set of facilities to open, as well as the assignments of each customer to exactly one of the open facilities so as to minimize the overall sum of costs. Similar to the negative preference in AP, the facility setup costs dictate the number of facilities that will be opened, and the problem can be viewed as that of finding an optimal trade-off between incurred customer-facility costs (negative pairwise similarities) and facility setup costs.
منابع مشابه
Hierarchical Topical Segmentation with Affinity Propagation
We present a hierarchical topical segmenter for free text. Hierarchical Affinity Propagation for Segmentation (HAPS) is derived from a clustering algorithm Affinity Propagation. Given a document, HAPS builds a topical tree. The nodes at the top level correspond to the most prominent shifts of topic in the document. Nodes at lower levels correspond to finer topical fluctuations. For each segment...
متن کاملMixture Modeling by Affinity Propagation
Clustering is a fundamental problem in machine learning and has been approached in many ways. Two general and quite different approaches include iteratively fitting a mixture model (e.g., using EM) and linking together pairs of training cases that have high affinity (e.g., using spectral methods). Pair-wise clustering algorithms need not compute sufficient statistics and avoid poor solutions by...
متن کاملBeyond Affinity Propagation: Message Passing Algorithms for Clustering
Beyond Affinity Propagation: Message Passing Algorithms for Clustering Inmar-Ella Givoni Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2012 Affinity propagation is an exemplar-based clustering method that takes as input similarities between data points. It outputs a set of data points that best represent the data (exemplars), and assignments of each non-exem...
متن کاملRecitation 6: Random forests and affinity propagation
– Partitioning clustering algorithms, construct non-overlapping clusters such that each item is assigned to exactly one cluster. Example: k-means – Agglomerative clustering algorithms construct a hierarchical set of nested clusters, indicating the relatedness between clusters. Example: hierarchical clustering • In classification, we partition data into known labels. For example, we might constr...
متن کاملA New Knowledge-Based System for Diagnosis of Breast Cancer by a combination of the Affinity Propagation and Firefly Algorithms
Breast cancer has become a widespread disease around the world in young women. Expert systems, developed by data mining techniques, are valuable tools in diagnosis of breast cancer and can help physicians for decision making process. This paper presents a new hybrid data mining approach to classify two groups of breast cancer patients (malignant and benign). The proposed approach, AP-AMBFA, con...
متن کاملClustering with shallow trees
We propose a new method for hierarchical clustering based on the optimisation of a cost function over trees of limited depth, and we derive a message–passing method that allows to solve it efficiently. The method and algorithm can be interpreted as a natural interpolation between two well-known approaches, namely single linkage and the recently presented Affinity Propagation. We analyze with th...
متن کامل