Clustering Uncertain Data Based on Probability Distribution Similarity

International Journal of Distributed Sensor Networks

et al. 2016

With the rapid development of WLAN infrastructure, fingerprint-based positioning using signal strength has become a promising localization solution in indoor space. Commonly fingerprint-based positioning methods face two challenges in large indoor space, one is floor recognition in large building with multifloor, and the other is signal strength variance due to heterogeneous devices and environmental factors. In this paper, we propose a novel two-stage positioning approach to address these challenges of fingerprintbased positioning methods in large indoor space. Firstly, we design a floor-level recognition feature based on WiFi access points and the RSS values to recognize floor. For solving the signal strength variance problem, we propose a new metric to capture the similarity of location fingerprints probability distribution using KL Divergence. To demonstrate the utility of our approach, we have performed comprehensive experiments in a large indoor building.

Section: The Second Stage: Indoor Positioning Using Wifi Rssmentioning

confidence: 99%

BigLoc: A Two-Stage Positioning Method for Large Indoor Space

Zheng

International Journal of Distributed Sensor Networks

et al. 2016

“…JSD is symmetric, non-negative, and bounded. It is widely used in the community of uncertain data mining [45]. The distribution difference J(U i ||U j ) between the data object U i and U j is defined as:…”

Section: Dissimilarity Estimationmentioning

confidence: 99%

Uncertainty-Aware Multidimensional Ensemble Data Visualization and Exploration

Zhang

IEEE Trans. Visual. Comput. Graphics

et al. 2015

Abstract-This paper presents an efficient visualization and exploration approach for modeling and characterizing the relationships and uncertainties in the context of multidimensional ensemble datasets. Its core is a novel dissimilarity-preserving projection technique that characterizes not only the relationships among the mean values of the ensemble data objects but also the relationships among the distributions of ensemble members. This uncertainty-aware projection scheme leads to an improved understanding of the intrinsic structure in an ensemble dataset. The analysis of the ensemble dataset is further augmented by a suite of visual encoding and exploration tools. Experimental results on both artificial and real-world datasets demonstrate the effectiveness of our approach.

“…The smaller the KL-divergence, the more similar the two distributions. It shows effectiveness to measure the similarity between uncertain objects in the paper [27] because the distribution difference cannot be captured by geometric distances directly. KLdivergence is a non-symmetric measurement, which means the difference from distribution P to Q is generally not the same as that from Q to P .…”

Section: Relationship Outliers and Categorizationmentioning

confidence: 99%

“…We borrow the idea from [27]. We apply the continuous case in our data set since the measurement in this thesis is continuous.…”

Section: Using Kl Divergence As Similaritymentioning

confidence: 99%

Multi-level relationship outlier detection

Jiang¹,

Campbell²,

Tang³

et al. 2012

IJBIDM

Self Cite

Relationship management is critical in business. Particularly, it is important to detect abnormal relationships, such as fraudulent relationships between service providers and consumers. Surprisingly, in the literature there is no systematic study on detecting relationship outliers. Particularly, no existing methods can detect and handle relationship outliers between groups and individuals in groups. In this thesis, we tackle this important problem by developing a simple yet effective model. We identify two types of outliers and devise efficient detection algorithms. Our experiments on both real data sets and synthetic ones confirm the effectiveness and efficiency of our approach.