A hybrid algorithm for k-medoid clustering of large data sets

In this paper, we propose a novel local search heuristic and then hybridize it with a genetic algorithm for k-medoid clustering of large data sets, which is an NP-hard optimization problem. The local search heuristic selects k-medoids from the data set and tries to efficiently minimize the total dissimilarity within each cluster. In order to deal with the local optimality, the local search heuristic is hybridized with a genetic algorithm and then the Hybrid K-medoid Algorithm (HKA) is proposed. Our experiments show that, compared with previous genetic algorithm based k-medoid clustering approaches - GCA and RAR/sub w/GA, HKA can provide better clustering solutions and do so more efficiently. Experiments use two gene expression data sets, which may involve large noise components

A niching memetic algorithm for simultaneous clustering and feature selection

Clustering is inherently a difficult task, and is made even more difficult when the selection of relevant features is also an issue. In this paper we propose an approach for simultaneous clustering and feature selection using a niching memetic algorithm. Our approach (which we call NMA_CFS) makes feature selection an integral part of the global clustering search procedure and attempts to overcome the problem of identifying less promising locally optimal solutions in both clustering and feature selection, without making any a priori assumption about the number of clusters. Within the NMA_CFS procedure, a variable composite representation is devised to encode both feature selection and cluster centers with different numbers of clusters. Further, local search operations are introduced to refine feature selection and cluster centers encoded in the chromosomes. Finally, a niching method is integrated to preserve the population diversity and prevent premature convergence. In an experimental evaluation we demonstrate the effectiveness of the proposed approach and compare it with other related approaches, using both synthetic and real data

From M-ary Query to Bit Query: a new strategy for efficient large-scale RFID identification

The tag collision avoidance has been viewed as one of the most important research problems in RFID communications and bit tracking technology has been widely embedded in query tree (QT) based algorithms to tackle such challenge. Existing solutions show further opportunity to greatly improve the reading performance because collision queries and empty queries are not fully explored. In this paper, a bit query (BQ) strategy based Mary query tree protocol (BQMT) is presented, which can not only eliminate idle queries but also separate collided tags into many small subsets and make full use of the collided bits. To further optimize the reading performance, a modified dual prefixes matching (MDPM) mechanism is presented to allow multiple tags to respond in the same slot and thus significantly reduce the number of queries. Theoretical analysis and simulations are supplemented to validate the effectiveness of the proposed BQMT and MDPM, which outperform the existing QT-based algorithms. Also, the BQMT and MDPM can be combined to BQMDPM to improve the reading performance in system efficiency, total identification time, communication complexity and average energy cost

The Fermi surface of underdoped high-T_c superconducting cuprates

The coexistence of $\pi$-flux state and d-wave RVB state is considered in this paper within the slave boson approach. A critical value of doping concentration $\delta_c$ is found, below which the coexisting $\pi$-flux and d-wave RVB state is favored in energy. The pseudo Fermi surface of spinons and the physical electron spectral function are calculated. A clear Fermi-level crossing is found along the (0,0) to ($\pi$, $\pi$) direction, but no such crossing is detected along the ($\pi$, 0) to ($\pi$, $\pi$) direction. Also, an energy gap of d-wave symmetry appears at the Fermi level in our calculation. The above results are in agreement with the angle-resolved photoemission experiments which indicate at a d-wave pseudo-gap and a half-pocket-like Fermi surface in underdoped cuprates.Comment: 18 pages RevTex, 6 figures in PS file

Non-canonical statistics of finite quantum system

The canonical statistics describes the statistical properties of an open system by assuming its coupling with the heat bath infinitesimal in comparison with the total energy in thermodynamic limit. In this paper, we generally derive a non-canonical distribution for the open system with a finite coupling to the heat bath, which deforms the energy shell to effectively modify the conventional canonical way. The obtained non-canonical distribution reflects the back action of system on the bath, and thus depicts the statistical correlations through energy fluctuations