We consider the problem of universal pooled steganalysis, in which we aim to identify a steganographer who sends many images (some of them innocent) in a network of many other innocent users. The detector must deal with multiple users and multiple images per user, and particularly the differences between cover sources used by different users. Despite being posed for five years, this problem has...

While significant work in data mining has been dedicated to the detection of single outliers in the data, less research has approached the problem of isolating a group of outliers, i.e. rare events representing micro-clusters of less – or significantly less – than 1% of the whole dataset. This research issue is critical for example in medical applications. The problem is difficult to handle as ...

The size and number of telecom databases are growing quickly but most of the data has not been analyzed for revealing the hidden and valuable intellectual. Models developed from data mining techniques are useful for telecom to make right prediction. The dataset contains one million customers from a telecom company. We implement data mining techniques, i.e., AdaboostM1 (ABM) algorithm, Naïve Bay...

Outlier detection has recently become an important problem in many industrial and financial applications. In this paper, a novel unsupervised algorithm for outlier detection with a solid statistical foundation is proposed. First we modify a nonparametric density estimate with a variable kernel to yield a robust local density estimation. Outliers are then detected by comparing the local density ...

Outlier detection is an important task in data mining that enjoys a wide range of applications such as detections of credit card fraud, criminal activity and exceptional patterns in databases. In recent years, there have been numerous research work in outlier detection and the new notions such as distance-based outliers and density-based local outliers have been proposed. However, the existing ...

Many studies of outlier detection have been developed based on the cluster-based outlier detection approach, since it does not need any prior knowledge of the dataset. However, the previous studies only regard the outlier factor computation with respect to a single point or a small cluster, which reflects its deviates from a common cluster. Furthermore, all objects within outlier cluster are as...

Anomaly detection, an important branch of machine learning, plays a critical role in fraud health care, intrusion military surveillance, etc. As one the most commonly used unsupervised anomaly detection algorithms, Local Outlier Factor algorithm (LOF algorithm) has been extensively studied. This contains three steps, i.e., determining k-distance neighborhood for each data point x, computing loc...

We investigate the use of biased sampling according to the density of the data set to speed up the operation of general data mining tasks, such as clustering and outlier detection in large multidimensional data sets. In density-biased sampling, the probability that a given point will be included in the sample depends on the local density of the data set. We propose a general technique for densi...

We investigate the use of biased sampling according to the density of the dataset, to speed up the operation of general data mining tasks, such as clustering and outlier detection in large multidimensional datasets. In densitybiased sampling, the probability that a given point will be included in the sample depends on the local density of the dataset. We propose a general technique for density-...