ARDV: A New Density Based Outlier Mining Approach
نویسندگان
چکیده
Local Outlier Factor (LOF) is an important and well known density based outliers handling algorithm, which quantifies, how much an object is outlying, in a given database. In this paper first we discuss LOF then we introduce the concept of ARDV. In LOF there is a concept of lrd (local reachability density). If in place of lrd we calculate ard (average reachability distance) and in place of LOF we calculate variance in ard (ARDV) then experimental results show that percentage of detecting correct outliers increases without increasing time complexity.
منابع مشابه
ON APPLICATIONS OF DENSITY TRANSFORMS FOR UNCERTAIN DATA MINING Applications to Classification and Outlier Detection
In this chapter, we will examine a general density-based approach for handling uncertain data. The broad idea is that implicit information about the errors can be indirectly incorporated into the density estimate. We discuss methods for constructing error-adjusted densities of data sets, and using these densities as intermediate representations in order to perform more accurate mining. We discu...
متن کاملA Framework for Outlier Detection in Geographic Spatial Data
Outlier detection is very interesting, useful and challenging problem in the field of data mining. Because of sparse data clustering algorithm which are based on distance will not work to find outliers in spatial data. Problem of finding irregular feature in spatial data need to be explore. Many existing approaches have been proposed to overcome the problem of outlier detection in spatial Geogr...
متن کاملOutlier Detection in Wireless Sensor Networks Using Distributed Principal Component Analysis
Detecting anomalies is an important challenge for intrusion detection and fault diagnosis in wireless sensor networks (WSNs). To address the problem of outlier detection in wireless sensor networks, in this paper we present a PCA-based centralized approach and a DPCA-based distributed energy-efficient approach for detecting outliers in sensed data in a WSN. The outliers in sensed data can be ca...
متن کاملA Novel Subspace Outlier Detection Approach in High Dimensional Data Sets
Many real applications are required to detect outliers in high dimensional data sets. The major difficulty of mining outliers lies on the fact that outliers are often embedded in subspaces. No efficient methods are available in general for subspace-based outlier detection. Most existing subspacebased outlier detection methods identify outliers by searching for abnormal sparse density units in s...
متن کاملRODHA: Robust Outlier Detection using Hybrid Approach
The task of outlier detection is to find the small groups of data objects that are exceptional to the inherent behavior of the rest of the data. Detection of such outliers is fundamental to a variety of database and analytic tasks such as fraud detection and customer migration. There are several approaches[10] of outlier detection employed in many study areas amongst which distance based and de...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2013