SimClus: an effective algorithm for clustering with a lower bound on similarity

Clustering algorithms generally accept a parameter k from the user, which determines the number of clusters sought. However, in many application domains, like document categorization, social network clustering, and frequent pattern summarization, the proper value of k is difficult to guess. An alternative clustering formulation that does not require k is to impose a lower bound on the similarity between an object and its corresponding cluster representative. Such a formulation chooses exactly one representative for every cluster and minimizes the representative count. It has many additional benefits. For instance, it supports overlapping clusters in a natural way. Moreover, for every cluster, it selects a representative object, which can be effectively used in summarization or semi-supervised classification task. In this work, we propose an algorithm, SimClus, for clustering with lower bound on similarity. It achieves a O(log n) approximation bound on the number of clusters, whereas for the best previous algorithm the bound can be as poor as O(n). Experiments on real and synthetic data sets show that our algorithm produces more than 40% fewer representative objects, yet offers the same or better clustering quality. We also propose a dynamic variant of the algorithm, which can be effectively used in an on-line setting.     

Evidence of short-range order in the disordered Cu-Ti alloys

Diffuse X-ray scattering measurements have been carried out on a series of Cu-Ti alloys, in order to determine the dependence of short-range order (SRO) parameters on the composition and annealing temperature. The diffuse scattering intensity (DSI) curves contain a maximum at the superstructural position 100 which provides evidence of SRO in this alloy. The degree of SRO has been estimated by calculating the Warren-Cowley parameter ₁ (WCP) and size-effect parameter ₁ from the DSI curves. The large negative values of WCP for all samples show the presence of heterogeneous SRO in these alloys. Comparison is also made between the values of experimental WCP and theoretical WCP calculated in the framework of electronic theory of short-range order and the high-temperature approximation of Clapp and Moss. A fairly good agreement between theory and experiment is observed.     

Three Dimensional Conceptualisation of Hydrogeological Environment to Underpin Groundwater Management in Irrigation Area

Australian irrigated agriculture utilises about 70?% of all water used in the country, 21?% of which is derived from groundwater. Sustainability for irrigated agriculture also depends on keeping the watertables at a safe level below the rootzone to avoid salinisation and reduction in crop yields. There is a vital need to understand groundwater and aquifer systems and their roles in the sustainability of irrigated agriculture in order to manage groundwater properly. This study builds on the previous hydrogeological and groundwater investigations of the Coleambally Irrigation Area (CIA) in New South Wales of Australia. It presents a new approach which systematically characterises regional hydrogeological environment using a three-dimensional (3-D) conceptual framework developed in ArcGIS. The 3-D hydrological conceptualisation of the CIA has integrated disparate sources of data into a coherent knowledge base for a better visualisation of hydrogeological characteristics and a comprehensive analysis of groundwater flow and aquifers. As an application example, the model was used to develop cross-sectional models of the area and to estimate regional-scale net recharge. The results have provided a basis for the numerical modelling and added values to procedures which underpin irrigation system management investment decisions through improving the understanding of hydrogeology underlying the area and creating an action-oriented dialogue among stakeholders.     

The utilization of soft transformation and genetic algorithm to model two sources of uncertainty of Indicator Kriging

Indicator Kriging (IK) is a geostatistical method that uses observation points to quantify the probabilities at which a set of thresholds are exceeded at unmeasured points. To improve IK accuracy, the interpolation process should consider its uncertainty sources. By doing this, we also maintain its ability to provide the conditional cumulative distribution function (ccdf), which is a reliable measure of local uncertainty. This study modeled two IK uncertainty sources, i.e., measurement errors attached to observation points and subjective threshold choices. Soft Indicator Kriging (SIK), which uses a soft transformation for observation points, considers the measurement errors of these two sources. To select the thresholds objectively, a genetic algorithm (GA) was performed to obtain the optimum set of thresholds related to an objective function, which minimized the mean absolute error (MAE).     

A case-based reasoning system development for statistical process control: Case representation and retrieval

Statistical process control (SPC) is a sub-area of statistical quality control. Considering the successful results of the SPC applications in various manufacturing and service industries, this field has attracted a large number of experts. Despite the development of knowledge in this field, it is hard to find a comprehensive perspective or model covering such a broad area and most studies related to SPC have focused only on a limited part of this knowledge area. According to many implemented cases in statistical process control, case-based reasoning (CBR) systems have been used in this study for developing of a knowledge-based system (KBS) for SPC to organize this knowledge area. Case representation and retrieval play an important role to implement a CBR system. Thus, a format for representing cases of SPC and the similarity measures for case retrieval are proposed in this paper.     

An optical solution to the 3-SAT problem using wavelength based selectors

In this paper, an optical wavelength-based method to solve a well-known NP-complete problem 3-SAT is provided. In the 3-SAT problem, a formula F in the form of conjunction of some clauses over Boolean variables is given and the question is to find if F is satisfiable or not. The provided method uses exponential number of different wavelengths in a light ray and considers each group of wavelengths as a possible value-assignment for the variables. It then uses optical devices to drop wavelengths not satisfying F from the light ray. At the end, remaining wavelengths indicate satisfiability of the formula. The method provides two ways to arrange the optical devices to select satisfying wavelengths for a given clause: simple clause selectors and combined clause selectors, both requiring exponential preprocessing time. After preprocessing phase, the provided method requires polynomial time and optical devices to solve each problem instance.