Distance Based Outlier for Data Streams Using Grid Structure
نویسندگان
چکیده
منابع مشابه
Distance-based Outlier Detection in Data Streams
Continuous outlier detection in data streams has important applications in fraud detection, network security, and public health. The arrival and departure of data objects in a streaming manner impose new challenges for outlier detection algorithms, especially in time and space efficiency. In the past decade, several studies have been performed to address the problem of distance-based outlier de...
متن کاملDBOD-DS: Distance Based Outlier Detection for Data Streams
Data stream is a newly emerging data model for applications like environment monitoring, Web click stream, network traffic monitoring, etc. It consists of an infinite sequence of data points accompanied with timestamp coming from external data source. Typically data sources are located onsite and very vulnerable to external attacks and natural calamities, thus outliers are very common in the da...
متن کاملOutlier Detection for Dynamic Data Streams Using Weighted K-means
This paper presents a new k-means type clustering algorithm that can calculate weights to the variables. This method is efficient for dynamic data streams in order to overcome the global optimum problems. The variable weights produced by the algorithm measures the importance of variable in clustering and can be used in variable selection in which the data items with similar properties are group...
متن کاملEntropy Based Adaptive Outlier Detection Technique for Data Streams
Outlier detection in data streams is an immensely enthralling problem in many application areas such as network intrusion detection, faulty sensor detection, fraud detection in online financial transactions etc. Majority of existing outlier detection techniques have been mainly designed for static datasets and require a global view and multiple scans of data which is not feasible in case of str...
متن کاملPrivacy-Preserving Outlier Detection for Data Streams
In cyber-physical systems sensors data should be anonymized at the source. Local data perturbation with differential privacy guarantees can be used, but the resulting utility is often (too) low. In this paper we contribute an algorithm that combines local, differentially private data perturbation of sensor streams with highly accurate outlier detection. We evaluate our algorithm on synthetic da...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Information Technology Journal
سال: 2009
ISSN: 1812-5638
DOI: 10.3923/itj.2009.128.137