Collaborative fuzzy clustering algorithms: some refinements and design guidelines

There are some variants of the widely used Fuzzy C-Means (FCM) algorithm that support clustering data distributed across different sites. Those methods have been studied under different names, like collaborative and parallel fuzzy clustering. In this study, we offer some augmentation of the two FCM-based clustering algorithms used to cluster distributed data by arriving at some constructive ways of determining essential parameters of the algorithms (including the number of clusters) and forming a set of systematically structured guidelines such as a selection of the specific algorithm depending on the nature of the data environment and the assumptions being made about the number of clusters. A thorough complexity analysis, including space, time, and communication aspects, is reported. A series of detailed numeric experiments is used to illustrate the main ideas discussed in the study.

A complex networks approach for data clustering

This work proposes a method for data clustering based on complex networks theory. A data set is represented as a network by considering different metrics to establish the connection between each pair of objects. The clusters are obtained by taking into account five community detection algorithms. The network-based clustering approach is applied in two real-world databases and two sets of artificially generated data. The obtained results suggest that the exponential of the Minkowski distance is the most suitable metric to quantify the similarities between pairs of objects. In addition, the community identification method based on the greedy optimization provides the best cluster solution. We compare the network-based clustering approach with some traditional clustering algorithms and verify that it provides the lowest classification error rate. (C) 2012 Elsevier B.V. All rights reserved.

Admissions due to pneumonia and biomass burning: a spatial approach

Objective: To identify spatial patterns in rates of admission for pneumonia among children and relate them to the number of fires reported in the state of Mato Grosso, Brazil. Methods: We conducted an ecological and exploratory study of data from the state of Mato Grosso for 2008 and 2009 on hospital admissions of children aged 0 to 4 years due to pneumonia and on fires in the same period. Admission rates were calculated and choropleth maps were plotted for rates and for fire outbreaks, Moran's I was calculated and the kernel estimator used to identify "hotspots." Data were analyzed using TerraView 3.3.1. Results: Fifteen thousand six hundred eighty-nine children were hospitalized (range zero to 2,315), and there were 161,785 fires (range 7 to 6,454). The average rate of admissions per 1,000 inhabitants was 2.89 (standard deviation [SD] = 5.18) and the number of fires per 1,000 inhabitants was 152.81 (SD = 199.91). Moran's I for the overall number of admissions was I = 0.02 (p = 0.26), the index for rate of admission was I = 0.02 (p = 0.21) and the index for the number of fires was I = 0.31 (p < 0.01). It proved possible to identify four municipalities with elevated rates of admissions for pneumonia. It was also possible to identify two regions with high admission densities. A clustering of fires was evident along what is known as the "arc of deforestation." Conclusions: This study identified municipalities in the state of Mato Grosso that require interventions to reduce rates of admission due to pneumonia and the number fires.

The SB-index and the HSB-index: efficient indices for spatial data warehouses

Spatial data warehouses (SDWs) allow for spatial analysis together with analytical multidimensional queries over huge volumes of data. The challenge is to retrieve data related to ad hoc spatial query windows according to spatial predicates, avoiding the high cost of joining large tables. Therefore, mechanisms to provide efficient query processing over SDWs are essential. In this paper, we propose two efficient indices for SDW: the SB-index and the HSB-index. The proposed indices share the following characteristics. They enable multidimensional queries with spatial predicate for SDW and also support predefined spatial hierarchies. Furthermore, they compute the spatial predicate and transform it into a conventional one, which can be evaluated together with other conventional predicates by accessing a star-join Bitmap index. While the SB-index has a sequential data structure, the HSB-index uses a hierarchical data structure to enable spatial objects clustering and a specialized buffer-pool to decrease the number of disk accesses. The advantages of the SB-index and the HSB-index over the DBMS resources for SDW indexing (i.e. star-join computation and materialized views) were investigated through performance tests, which issued roll-up operations extended with containment and intersection range queries. The performance results showed that improvements ranged from 68% up to 99% over both the star-join computation and the materialized view. Furthermore, the proposed indices proved to be very compact, adding only less than 1% to the storage requirements. Therefore, both the SB-index and the HSB-index are excellent choices for SDW indexing. Choosing between the SB-index and the HSB-index mainly depends on the query selectivity of spatial predicates. While low query selectivity benefits the HSB-index, the SB-index provides better performance for higher query selectivity.