Clustered Data

Dividing a data set into a training set and a test set is a fundamental component in the preprocessing phase of data mining (DM). Effectively, the choice of the training set is an important factor in deriving good classification rules. Traditional approach for association rules mining divides the dataset into training set and test set based on statistical methods. In

A Petschek-type model of magnetic reconnection is used to describe the behaviour of nightside flux transfer events (NFTEs). Based on the Cagniard-deHoop method we calculate the magnetic field and plasma flow time series observed by a satellite. The reconstruction of the reconnection electric field is an ill-posed inverse problem, which we treat with the method of regularisation, since the solution of the Cagniard-deHoop method is given in the form of a convolution integral, which is a well- known problem in the theory of inverse problems. This method is applied to Cluster measurements from September 8 th , 2002, and from August, 13 th , 2002, where a series of Earth-ward propagating 1-minute scale magnetic field and plasma flow variations are observed which are consistent with the theoretical picture of NFTEs. Methods to estimate the satellite position with respect to the reconnection site as well as the Alfven velocity are presented because they are necessary parameters used in the...

We discuss types of clustering problems where error information associated with the data to be clustered is readily available and where error-based clustering is likely to be superior to clustering methods that ignore error. We focus on clustering derived data (typically parameter ...

Geostatistical estimation and simulation techniques are useful for characterizing the spatial distribution of soil properties. These techniques usually rely on a variogram model that measures the spatial variability of the property under study. However, uneven sampling designs often make the traditional sample variogram lose accuracy and tend to mask the spatial structure. In this paper, a methodology is proposed to

This paper presents quadratic integer programming as a modeling technique to formulate the analytical models for financial markets. Financial market analysis has been a promising area of research for the last three decades. The proposed models aim to cater for several scenarios for analysis and prediction of future trends in capital, gold and currency markets. These models after translation into hyper graphs would be solved through clustering (data mining). The implementation of this research would be the basis of a decision support system. It may prove to be an analysis tool to evaluate the economic status of a country statistically in a more accurate manner

This paper proposes feasible nonparametric random effects estimators. Specifically, we propose feasible versions of the two estimators in Lin and Carroll (2000) [Lin, X. and RJ Carroll, 2000, Nonparametric function estimation for clustered data when the predictor is ...

One of the main problems in cluster analysis is the weighting of attributes so as to discover structures that may be present. By using weighted dissimilarity measures for objects, a new approach is developed, which allows the use of the k-means-type paradigm to efficiently cluster large data sets. The optimization algorithm is presented and the effectiveness of the algorithm is demonstrated with both synthetic and real data sets.

CMDs are the most popular tool for analyzing resolved stellar populations. However, due to degeneracies among Teff, [Fe/H], and reddening in traditional CMDs, it can be difficult to draw robust conclusions from the data. The 5-band system used for the bulge Treasury observations will provide indices that are roughly orthogonal in Teff and [Fe/H], and we argue that model fitting in an index-index diagram will make better use of the information than fitting separate CMDs. We provide simulations to show the expected data quality and the potential for differentiating between different star-formation histories.

Conventional statistical downscaling techniques for prediction of multi-site rainfall in a river basin fail to capture the correlation between multiple sites and thus are inadequate to model the variability of rainfall. The present study addresses this problem through representation of the pattern of multi-site rainfall using rainfall state in a river basin. A model based on K-means clustering technique coupled with a supervised data classification technique, namely Classification And Regression Tree (CART), is used for generation of rainfall states from large-scale atmospheric variables in a river basin. The K-means clustering is used to derive the daily rainfall state from the historical daily multi-site rainfall data. The optimum number of clusters in the observed rainfall data is obtained after application of various cluster validity measures to the clustered data. The CART model is then trained to establish relationship between the daily rainfall state of the river basin and the standardized, dimensionally-reduced National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis climatic data set. The relationship thus developed is applied to the General Circulation Model (GCM)-simulated, standardized, bias free large-scale climate variables for prediction of rainfall states in future. Comparisons of the number of days falling under different rainfall states for the observed period and the future give the change expected in the river basin due to global warming. The methodology is tested for the Mahanadi river basin in India.

Data mining aims at extraction of previously unidentified information from large databases. It can be viewed as an automated application of algorithms to discover hidden patterns and to extract knowledge from data. Online Analytical Processing (OLAP) systems, on the other hand, allow exploring and querying huge datasets in interactive way. These OLAP systems are the predominant front-end tools used in data warehousing environments and the OLAP system's market has developed rapidly during the last few years. Several ...