A New Dissimilarity Measure for Trajectories with Applications in Anomaly Detection

نویسندگان

  • Dustin Luis Espinosa-Isidrón
  • Edel B. García Reyes
چکیده

Trajectory clustering has been used to very effectively in the detection of anomalous behavior in video sequences. A key point in trajectory clustering is how to measure the (dis)similarity between two trajectories. This paper deals with a new dissimilarity measure for trajectory clustering, giving the same importance to differences and similarities between the trajectories. Experimental results in the task of anomalous detection via hierarchical clustering shows the validity of the proposed approach.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

خوشه‌بندی داده‌های بیان‌ژنی توسط عدم تشابه جنگل تصادفی

Background: The clustering of gene expression data plays an important role in the diagnosis and treatment of cancer. These kinds of data are typically involve in a large number of variables (genes), in comparison with number of samples (patients). Many clustering methods have been built based on the dissimilarity among observations that are calculated by a distance function. As increa...

متن کامل

Anomaly-based Web Attack Detection: The Application of Deep Neural Network Seq2Seq With Attention Mechanism

Today, the use of the Internet and Internet sites has been an integrated part of the people’s lives, and most activities and important data are in the Internet websites. Thus, attempts to intrude into these websites have grown exponentially. Intrusion detection systems (IDS) of web attacks are an approach to protect users. But, these systems are suffering from such drawbacks as low accuracy in ...

متن کامل

Moving dispersion method for statistical anomaly detection in intrusion detection systems

A unified method for statistical anomaly detection in intrusion detection systems is theoretically introduced. It is based on estimating a dispersion measure of numerical or symbolic data on successive moving windows in time and finding the times when a relative change of the dispersion measure is significant. Appropriate dispersion measures, relative differences, moving windows, as well as tec...

متن کامل

Combining Disparate Information for Machine Learning

Combining Disparate Information for Machine Learning by Ko-Jen Hsiao Chair: Alfred O. Hero This thesis considers information fusion for four different types of machine learning problems: anomaly detection, information retrieval, collaborative filtering and structure learning for time series, and focuses on a common theme – the benefit to combining disparate information resulting in improved alg...

متن کامل

The efficiency of conformal predictors for anomaly detection

This thesis explores the application of conformal prediction to the anomaly detection domain. Anomaly detection is a large area of research in machine learning and many interesting techniques have been developed to detect ‘abnormal’ behaviour of objects that do not conform to typical behaviour. Recently conformal predictors (CP) have emerged which allow the detection of the non-conformal behavi...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2010