Mansi Ghodsi

A review of data mining applications in crime

Crime continues to remain a severe threat to all communities and nations across the globe alongside the sophistication in technology and processes that are being exploited to enable highly complex criminal activities. Data mining, the process of uncovering hidden information from Big Data, is now an important tool for investigating, curbing and preventing crime and is exploited by both private and government institutions around the world. The primary aim of this paper is to provide a concise review of the data mining applications in crime. To this end, the paper reviews over 100 applications of data mining in crime, covering a substantial quantity of research to date, presented in chronological order with an overview table of many important data mining applications in the crime domain as a reference directory. The data mining techniques themselves are briefly introduced to the reader and these include entity extraction, clustering, association rule mining, decision trees, support vector machines, naive Bayes rule, neural networks and social network analysis amongst others.

A study on the empirical distribution of the scaled Hankel matrix eigenvalues.

The empirical distribution of the eigenvalues of the matrix XXT divided by its trace is evaluated, where X is a random Hankel matrix. The distribution of eigenvalues for symmetric and nonsymmetric distributions is assessed with various criteria. This yields several important properties with broad application, particularly for noise reduction and filtering in signal processing and time series analysis.

Exchange rate forecasting with optimum singular spectrum analysis

Forecasting exchange rate is undoubtedly an attractive and challenging issue that has been of interest in different domains for many years. The singular spectrum analysis (SSA) technique has been used as a promising technique for time series forecasting including exchange rate series. The SSA technique is based upon two main choices: Window length, L, and the number of singular values, r. These values are very important for the reconstruction stage and forecasting purposes. Here the authors consider an optimum version of the SSA technique for forecasting exchange rates. The forecasting performances of the SSA technique for one-step-ahead forecast of six exchange rate series are used to find the best L and r.

Distinguishing chaos from noise: A new approach

Literature suggests that distinguishing chaos from noise continues to remain a highly contentious issue in the modern age as it has been historically. This is because chaos and noise share common properties which in turn makes it indistinguishable. In this paper, we seek to provide a viable solution to this problem by presenting a novel approach for the differentiating and identifying chaos from noise. The proposed approach is one that is based on dynamical systems, embedding theorem, matrix algebra and statistical theory. To achieve our objective, the distribution, pattern and behaviour of eigenvalues are analysed in-depth. This yields several important properties with broad application, enabling the distinction between chaos and noise in time series analysis. The applicability of the proposed approach is examined using WTI Spot Price time series.

The Empirical Distribution of the Singular Values of a Random Hankel Matrix

The empirical distribution of the eigenvalues of the matrix HHT divided by its trace is considered, where H is a Hankel random matrix. The normal distribution with different parameters are considered and the effect of scale and shape parameters are evaluated. The correlation among eigenvalues are assessed using parametric and non-parametric association criteria.

Vector and recurrent singular spectrum analysis: which is better at forecasting?

ABSTRACT Singular spectrum analysis (SSA) is an increasingly popular and widely adopted filtering and forecasting technique which is currently exploited in a variety of fields. Given its increasing application and superior performance in comparison to other methods, it is pertinent to study and distinguish between the two forecasting variations of SSA. These are referred to as Vector SSA (SSA-V) and Recurrent SSA (SSA-R). The general notion is that SSA-V is more robust and provides better forecasts than SSA-R. This is especially true when faced with time series which are non-stationary and asymmetric, or affected by unit root problems, outliers or structural breaks. However, currently there exists no empirical evidence for proving the above notions or suggesting that SSA-V is better than SSA-R. In this paper, we evaluate out-of-sample forecasting capabilities of the optimised SSA-V and SSA-R forecasting algorithms via a simulation study and an application to 100 real data sets with varying structures, to provide a statistically reliable answer to the question of which SSA algorithm is best for forecasting at both short and long run horizons based on several important criteria.

An enhanced version of Cochran-Armitage trend test for genome-wide association studies

Genome-wide association studies the evaluation of association between candidate gene and disease status is widely carried out using Cochran-Armitage trend test. However, only a small number of research papers have evaluated the distribution of p-values for the Cochran-Armitage trend test. In this paper, an enhanced version of Cochran-Armitage trend test based on bootstrap approach is introduced. The achieved results confirm that the distribution of p-values of the proposed approach fits better to the uniform distribution, and it is thus concluded that the proposed method, which needs less assumptions in comparison with the conventional method, can be successfully used to test the genetic association.