. One method for detecting fraud is to check for suspicious changes in user behavior. This paper describes the automatic design of user profiling methods for the purpose of fraud detection, using a series of data mining techniques. Specifically, we use a rule-learning program to uncover indicators of fraudulent behavior from a large database of customer transactions. Then the indicators are used to create a set of monitors, which profile legitimate customer behavior and indicate anomalies. Finally, the outputs of the monitors are used as features in a system that learns to combine evidence to generate high-confidence alarms. The system has been applied to the problem of detecting cellular cloning fraud based on a database of call records. Experiments indicate that this automatic approach performs better than hand-crafted methods for detecting fraud. Furthermore, this approach can adapt to the changing conditions typical of fraud detection environments. Keywords: fraud detection, rule l...

Abstract. One method for detecting fraud is to check for suspicious changes in user behavior. This paper describes the automatic design of user profiling methods for the purpose of fraud detection, using a series of data mining techniques. Specifically, we use a rule-learning program to uncover indicators of fraudulent behavior from a large database of customer transactions. Then the indicators are used to create a set of monitors, which profile legitimate customer behavior and indicate anomalies. Finally, the outputs of the monitors are used as features in a system that learns to combine evidence to generate high-confidence alarms. The system has been applied to the problem of detecting cellular cloning fraud based on a database of call records. Experiments indicate that this automatic approach performs better than hand-crafted methods for detecting fraud. Furthermore, this approach can adapt to the changing conditions typical of fraud detection environments.

This paper describes the automatic design of methods for detecting fraudulent behavior. Much of the design is accomplished using a series of machine learning methods. In particular, we combine data mining and constructive induction with more standard machine learning techniques to design methods for detecting fraudulent usage of cellular telephones based on profiling customer behavior. Specifically, we use a rulelearning program to uncover indicators of fraudulent behavior from a large database of cellular calls. These indicators are used to create profilers, which then serve as features to a system that combines evidence from multiple profilers to generate high-confidence alarms. Experiments indicate that this automatic approach performs nearly as well as the best hand-tuned methods for detecting fraud. Introduction In the United States, cellular fraud costs the telecommunications industry hundreds of millions of dollars per year (Walters & Wilkinson 1994). A specific kind of cellul...

Abstract: Quality monitoring and fault detection are essential parts in automated electronics manufacturing systems. Information about process conditions enables operations to improve quality and increase throughput. This report presents a general quality monitoring framework and method for a manufacturing system. Proposed monitoring approach is an integration of model-based methods with systematically collected expert knowledge and data. A model bank is constructed to reproduce behaviour of the normal and fault states. The data driven normal condition model contains linguistic equation- non-linear scaling method for model variables, and a recursive gradient algorithm. Fuzzy reasoning and basic statistical methods are combined to identify changes in normal model residuals. Fault models are fuzzy rules for detecting abnormalities in selected time series signal. Analysed model outputs are then applied to monitoring task. Principles of the monitoring method are briefly discussed and demonstrated with a simulation example. Modelling results indicate that the proposed method can handle noise in simulation data. Generalisation ability of the normal model was also notified. Based on simulations, presented monitoring approach was

High frequency forecasts have always been considered difficult to generate and generation of forecast distributions instead of point forecasts has always been desired by the practitioners in asset allocation and investment management fields. This thesis looks at the problem of high frequency forecast generation by extending the traditional nonlinear dynamics technique by Farmer and Sidorowich and implementing it in a new connectionist architecture based on Correlation Matrix Memories. The architecture employs distribution generation and produces point forecasts as maximum probability points. Distributions are generated from nearest neighbour interpolations by a Bayesian technique. Testing of the architecture's utility has been performed by looking at a selection of representative time series including currency, index and commodity series and their forecast implied efficiency which was compared to standard efficiency estimates. Efficiency was measured with respect to Henriksson--Merton ...

Traditional methods usually encounter the problem in which the predicted results cannot reach a satisfactory need because the overshooting prediction value made by the forecasting model cause a big residual error at the turning points where the peak or valley observed values occurred. Therefore, this study introduced a intelligent prediction algorithm (including two types) utilized for the applications of non-periodic short-term time series forecast. This algorithm actually is a hybrid model, combing a grey prediction model and a cumulative least squared linear prediction model, with the technique of automatically compensating a possible overestimated predicted value by a potential damped predicted value for those predicting points having extreme peak or valley value. The verification of this study is also tested successfully in three experiments whose are stock price index, economy growth rate forecasts, and typhoon moving trace. Furthermore, the results of this intelligent algorithm also concluded that the proposed one achieved the best accuracy of predicted values in these experiments when compared with other five traditional forecasting models discussed in the experiments.

The paperdescri es the use ofstati"q=:5 siati selectiq i DNAmiq)55(7 y dataanalysiG The experi7G ts consi6"q= produce tiq(:(R"q= data. A commonly used method ofthresholdi7 i compared tousi: the `run test',`turni( poi t test',`Kendall's tau test' and `autocorrelatiq functiati We conclude that the use of such techni(G"( developed forti)G))"q=: analysiq can help to di(75"q=:) betweennon-i(7q=:)"R e si(RG) ornoi7 andsi)"5" whi h show ei::5 a trend or are correlated overtiRR 1 DNAMicro3' y Data For the dataconsiR"(q i thi paper we assume that geneexpressiq profiles were obtai6R from tio course DNAmiq7"56: y experi(" ts. Toi56R)q=)" thei dea andwiq)RR loss of generali y, we use the yeast data setdescri ed byEi)" et. al [2] and choose the 18 measurements obtai6G for the cell di7(::R cycle after synchroni)()q= by alpha factor arrest. In general, a geneexpressi= datamatri X has i =1, ..., n rows for the genes and j =1, ..., r columns for samples. Here we have n = 2467 yeast genes and r = 18ti"...

The paperdescri es the use ofstati"q=:5 siati selectiq i DNAmiq)55(7 y dataanalysiG The experi7G ts consi6"q= produce tiq(:(R"q= data. A commonly used method ofthresholdi7 i compared tousi: the `run test',`turni( poi t test',`Kendall's tau test' and `autocorrelatiq functiati We conclude that the use of such techni(G"( developed forti)G))"q=: analysiq can help to di(75"q=:) betweennon-i(7q=:)"R e si(RG) ornoi7 andsi)"5" whi h show ei::5 a trend or are correlated overtiRR 1 DNAMicro3' y Data For the dataconsiR"(q i thi paper we assume that geneexpressiq profiles were obtai6R from tio course DNAmiq7"56: y experi(" ts. Toi56R)q=)" thei dea andwiq)RR loss of generali y, we use the yeast data setdescri ed byEi)" et. al [2] and choose the 18 measurements obtai6G for the cell di7(::R cycle after synchroni)()q= by alpha factor arrest. In general, a geneexpressi= datamatri X has i =1, ..., n rows for the genes and j =1, ..., r columns for samples. Here we have n = 2467 yeast genes and r = 18ti"G7 oi ts. Each row vector x i =[x i1 , ..., x ij , ..., x ir ] represents a parti""q= geneexpressiq profile. Throughout the paper we refer to a gene byi:(row)numberi nX. x ij i the gene expressi= level atti7 j of gene i. x ij x i i the normaliq= log 2rati E ij/R ij where E ij i the expressi= level or state atti) j of the gene i,andR ij i the reference state of the gene, whi hi s a constant value throughout the experi() t [1]: x ij = # 18 k=1 i =1,...,2467 j =1,...,18 (1) Wi) the normali=75G5 of (1), x ij i posi"( e when E ij ij and we say the genei ieq:": or "up-regulated". When E ij ij , x ij i negati e and i isai that the genei repressed or "down-regulated". Inthi paper we assume that there are nomi(5"6 values,iues measurements have been obtaiq " for allsampli" poi...

Abstract—The issue of modelling and forecasting the share prices of the banking sector remains a challenge because of high volatilities in individual stock prices. Reliably forecasting the future values of shares is essential to minimize the risk for investors, but there is currently no standard forecasting procedure or technique that can be used in modelling and forecasting the share prices of the banking sector. This research is concerned with the development of a forecasting algorithm that can be applied in modelling and forecasting the share prices of the banking sector. It proposes six steps that, when followed, may lead to obtaining superior models. These