Data Mining Techniques for Fraud Detection

The paper presents application of data mining techniques to fraud analysis. We present some classification and prediction data mining techniques which we consider important to handle fraud detection. There exist a number of data mining algorithms and we present statistics-based algorithm, decision tree-based algorithm and rule-based algorithm. We present Bayesian classification model to detect fraud in automobile insurance. Naïve Bayesian visualization is selected to analyze and interpret the classifier predictions. We illustrate how ROC curves can be deployed for model assessment in order to provide a more intuitive analysis of the models. Keywords: Data Mining, Decision Tree, Bayesian Network, ROC Curve, Confusion Matri

Data Mining Techniques in Fraud Detection

The paper presents application of data mining techniques to fraud analysis. We present some classification and prediction data mining techniques which we consider important to handle fraud detection. There exist a number of data mining algorithms and we present statistics-based algorithm, decision treebased algorithm and rule-based algorithm. We present Bayesian classification model to detect fraud in automobile insurance. Naïve Bayesian visualization is selected to analyze and interpret the classifier predictions. We illustrate how ROC curves can be deployed for model assessment in order to provide a more intuitive analysis of the models

Exploring the solar poles: the last great frontier of the sun

Observations of the Sun’s poles is fundamental to understanding and predicting the solar cycle, constraining polar kilo-Gauss flux patches and plasma jets and illuminating the origin of the fast solar wind. This white paper argues the case for novel out-of-ecliptic observations of the Sun’s polar region in conjunction with existing or future multi-vantage point heliospheric observatories

Data Mining Techniques in Fraud Detection

The paper presents application of data mining techniques to fraud analysis. We present some classification and prediction data mining techniques which we consider important to handle fraud detection. There exist a number of data mining algorithms and we present statistics-based algorithm,   decision tree-based algorithm and rule-based algorithm. We present Bayesian classification model to detect fraud in automobile insurance.  Naïve Bayesian visualization is selected to analyze and interpret the classifier predictions. We illustrate how ROC curves can be deployed for model assessment in order to provide a more intuitive analysis of the models

Zoned Monazite and Zircon as Monitors for the Thermal History of Granulite Terranes: an Example from the Central Indian Tectonic Zone

The growth and dissolution behaviour of detrital, metamorphic and magmatic monazite and zircon during granulite-facies anatexis in pelitic and psammo-pelitic granulites and in garnetiferous granite from the southern margin of the Central Indian Tectonic Zone (CITZ) have been investigated using reconstructed metamorphic reaction history, monazite electron microprobe dating and sensitive high-resolution ion microprobe (SHRIMP) U–Pb zircon geochronology. Whereas the pelitic granulites record medium-pressure granulite-facies metamorphism (BM1 stage), the psammo-pelitic granulite reached ultrahigh temperatures (TMax > 880°C at 8·7 kbar). The meta-psammite additionally records two stages of granulite-facies recrystallization (BM2 and BM3). Irrespective of variations in the bulk-rock compositions and peak metamorphic conditions, monazite is highly reactive during the BM1 event, producing complex, chemically zoned crystals. Textural, compositional and chemical ages of these grains indicate the stability of six compositional domains (CD1 to CD6 in the paragenetic sequence), of which CD1 represents pre-metamorphic detrital cores of Paleoproterozoic age. CD2 and CD3 (combined mean age of 1612 ± 14 Ma) mark two stages of recrystallization of detrital monazite cores during prograde events. Rims of CD4 monazite (ages between 1615 ± 14 and 1586 ± 14 Ma) on partially to completely equilibrated cores indicate melt crystallization at, or immediately following, peak BM1P metamorphism. CD5 monazite (age of 1574 ± 7 Ma) is restricted to the psammo-pelitic granulites, and marks final melt crystallization at the solidus during post-peak cooling (BM1R stage, where R represents retrograde metamorphism). The metamorphic rim of CD6 monazite (age of 1539 ± 24 Ma) around partially resorbed CD5 domains is linked to the decomposition of BM1 garnet during the terminal hydration event as part of a granulite-facies recrystallization event. Compositionally homogeneous monazite and rims of chemically zoned monazite grains in granite together record a magmatic crystallization age of 1604 ± 9 Ma. SHRIMP U–Pb zircon dating of the psammo-pelitic granulite and garnetiferous granite indicates detrital or inherited cores of Paleo- to Neoarchean age (3584 ± 3 to 2530 ± 3 Ma), which have been variously recrystallized and overgrown by new zircon: (1) at 1658 ± 12 Ma; (2) between 1595 ± 5 and 1590 ± 6 Ma; (3) at 1574 ± 9 Ma. These zircon dates are correlated with the timing of the following: (1) the protoliths of precursor sediments of the metasedimentary granulites, deposited between 2530 and 1658 Ma; (2) a short-lived high-grade event ∼65–70 Myr before the culmination of the BM1 granulite-facies event; (3) a high-T anatectic event, corresponding to the peak BM1P metamorphism at TMax > 900°C; (4) final crystallization of anatectic melt at the solidus (cf. BM1R metamorphic stage). These chronological constraints from monazite and zircon, when integrated with the metamorphic reaction history and published geochronological data, allow recognition of three episodes of granulite-facies metamorphism in the CITZ at 1658 Ma (pre-BM1 event), between 1612 and 1574 Ma (BM1 event), and between 1572 and 1539 Ma (combined BM2 and BM3 events), as part of a latest Paleoproterozoic to Early Mesoproterozoic orogenic event. This orogeny is linked to the growth of the Proto-Greater Indian Landmass