2968463
Making Data Mining Models Useful to Model Non-paying Customers of Exchange Carriers
Due to both limitations of technologies and the nature of the problems, data mining may not be able to solve a problem completely in a way as one wishes. When this happens, we need to first understand the actual need of business, characteristic of available partial solution, and then make compromises between the technology solution and business needs. A majority of the papers published in data mining conferences and journals seem to concentrate only on the success side of the story. In this paper, we discuss our experiences and the complete process from near failure to success when applying inductive learning techniques to predict non-paying customers of competitive local exchange carriers (CLEC’s), currently at 20%. Experiments with a number of stateof-the-art methods and algorithms found that most customers were labeled as paying on time. Cost-sensitive learning is not possible since the target company cannot define a cost-model. Finally, after discussing with the billing department, a compromised but still useful solution is to predict the probability that someone will default. The billing team can use the predicted score to prioritize collection efforts as well as to predict cash flow. We have found that two randomized decision tree ensemble methods (Fan’s random decision tree and a probabilistic extension of Breiman’s random forest) are consistently more accurate in posterior probability estimation than single decision tree based probability calibration methods. The software, both Fan’s RDT and probabilistic extension of random forest, as well as a longer version of this paper will be made available by the contacting author.
8614779
AITVS: Advanced Interactive Traffic Visualization System
Transportation and the highway network form the backbone of the total public infrastructure system. As such, planning and monitoring for an effective transportation system is crucial in the building and maintenance of a region’s economy and safety. However, demand for road travel continues to expand as population increases (particularly in the metropolitan areas) while new constructions have not kept pace. According to the Federal Highway Administration, it is forecasted that the volume of freight movement alone is to nearly double by 2020 [1]. Congestion and looming gridlock crises seriously threaten any region’s mobility, safety and economic vitality. A crucial component in addressing these concerns is the development of specific technologies to monitor, model, and optimize traffic flow.
17196005
Analysis of payment transaction security in mobile commerce
Mobile payment is the process of two parties exchanging financial value using a mobile device in return for goods or services. This paper is an analysis of the security issues in mobile payment for m-commerce. We introduce m-commerce and mobile payment, discuss the public key infrastructure as a basis for secure mobile technologies, and study the features for different security technologies employed in current m-commerce market, including WAP, SIM application toolkit and J2M. In addition, we compare the effectiveness of these security technologies in supporting a secure mobile payment, and discuss research issues to enhance the security of mobile payment for large scale deployment of m-commerce.
3228880
Wavelet fuzzy classification for detecting and tracking region outliers in meteorological data
In this paper, a wavelet fuzzy classification approach is proposed to detect and track region outliers in meteorological data. First wavelet transform is applied to meteorological data to bring up distinct patterns that might be hidden within the original data. Then a powerful image processing technique, edge detection with competitive fuzzy classifier, is extended to identify the boundary of region outlier. After that, to determine the center of the region outlier, the fuzzy-weighted average of the longitudes and latitudes of the boundary locations is computed. By linking the centers of the outlier regions within consecutive frames, the movement of a region outlier can be captured and traced. Experimental evaluation was conducted on a real-world meteorological data to examine the effectiveness of the proposed approach. This work will help discover interesting and implicit information for large volume of meteorological data.
18415730
Multivariate Spatial Outlier Detection
A spatial outlier is a spatially referenced object whose non-spatial attribute values are significantly different from the values of its neighborhood. Identification of spatial outliers can lead to the discovery of unexpected, interesting, and useful spatial patterns for further analysis. Previous work in spatial outlier detection focuses on detecting spatial outliers with a single attribute. In the paper, we propose two approaches to discover spatial outliers with multiple attributes. We formulate the multi-attribute spatial outlier detection problem in a general way, provide two effective detection algorithms, and analyze their computation complexity. In addition, using a real-world census data, we demonstrate that our approaches can effectively identify local abnormality in large spatial data sets.
14730448
Detecting spatial outliers with multiple attributes
A spatial outlier is a spatially referenced object whose non-spatial attribute values are significantly different from the values of its neighborhood. Identification of spatial outliers can lead to the discovery of unexpected, interesting, and useful spatial patterns for further analysis. Previous work in spatial outlier detection focuses on detecting spatial outliers with a single attribute. In the paper, we propose two approaches to discover spatial outliers with multiple attributes. We formulate the multi-attribute spatial outlier detection problem in a general way, provide two effective detection algorithms, and analyze their computation complexity. In addition, using a real-world census data, we demonstrate that our approaches can effectively identify local abnormality in large spatial data sets.
15221618
Weighted efficient domination problem on some perfect graphs
Abstract Given a simple graph G =( V , E ), a vertex v ∈ V is said to dominate itself and all vertices adjacent to it. A subset D of V is called an efficient dominating set of G if every vertex in V is dominated by exactly one vertex in D . The efficient domination problem is to find an efficient dominating set of G with minimum cardinality. Suppose that each vertex v ∈ V is associated with a weight. Then, the weighted efficient domination problem is to find an efficient dominating set with the minimum weight in G . In this paper, we show that the efficient domination problem is NP-complete for planar bipartite graphs and chordal bipartite graphs. Assume that a permutation diagram of a bipartite permutation graph and a one-vertex-extension ordering of a distance-hereditary graph are given in advance. Then, we give O (| V |) time algorithms for the weighted efficient domination problem on bipartite permutation graphs and distance-hereditary graphs.
16028942
On monotone planar circuits
In this paper we show several results about monotone planar circuits. We show that monotone planar circuits of bounded width, with access to negated input variables, compute exactly the functions in non-uniform AC/sup 0/. This provides a striking contrast to the non-planar case, where exactly NC/sup 1/ is computed. We show that the circuit value problem for monotone planar circuits, with inputs on the outerface only, can be solved in LOGDCFL/spl sube/SC, improving a LOGCFL upper bound due to Dymond and Cook. We show that for monotone planar circuits, with inputs on the outerface only, excessive depth compared to width is useless; any function computed by a monotone planar circuit of width w with inputs on the outerface can be computed by a monotone planar circuit of width O(w) and depth w/sup O(1)/. Finally, we show that monotone planar read-once circuits, with inputs on the outerface only, can be efficiently learned using membership queries.
11974123
Practical 3-D Shape Measurement Using Optimal Intensity-Modulated Projection and Intensity-Phase Analysis Techniques
In the field of 3D image measurement, a technique based on stripe pattern projection and observation image intensity analysis is expected to be able to detect several stripes by a single projection. It is necessary to increase the stripe number of the projection pattern in order to improve the measurement accuracy of depth distance. However, when the stripe number is increased, the difference of the intensity between stripes will be reduced and stripe detection will become difficult. In order to improve the detection accuracy of the stripe order and shorten the 3D measurement time, we use the optimal intensity-modulation projection (OIMP) technique, and in order to improve the depth distance measurement accuracy, we propose an intensity-phase analysis (IPA) technique. In the proposed IPA technique, the observation pattern be segmented by the intensity of the intensity-modulated stripe, and in every segmentation the depth distance of all pixels are obtained by phase analysis. By using a combination of the OIMP and IPA techniques, high-speed (single projection and double image captures) and high-accuracy (error less than 0.1%) practical 3D shape measurement can be realized
15346536
Distance-two labelings of graphs
For given positive integers j ≥ k, an L(j,k)-labeling of a graph G is a function f : V (G) → {0, 1, 2,...} such that |f(u) - f(v)| ≥ j when dG(u, v) = 1 and |f(u) - f(v)| ≥ k when dG(u, v) = 2. The L(j, k)-labeling number λj,k(G) of G is defined as the minimum m such that there is an L(j, k)-labeling f of G with f(V(G)) ⊆ {0, 1, 2,....,m}. For a graph G of maximum degree Δ ≥ 1 it is the case that λj,k(G) ≥ j + (Δ - 1)k. The purpose of this paper is to study the structures of graphs G with maximum degree Δ ≥ 1 and λj,k(G) = j + (Δ - 1)k.
17738179
Perfect edge domination and efficient edge domination in graphs
Let G = (V,E) be a finite and undirected graph without loops and multiple edges. An edge is said to dominate itself and any edge adjacent to it. A subset D of E is called a perfect edge dominating set if every edge of E \ D is dominated by exactly one edge in D and an efficient edge dominating set if every edge of E is dominated by exactly one edge in D. The perfect (efficient) edge domination problem is to find a perfect (efficient) edge dominating set of minimum size in G. Suppose that each edge e is associated with a real number w(e) as its weight. Then, the weighted perfect (efficient) edge domination problem is to calculate a perfect (efficient) edge dominating set D such that the weight w(D) of D is minimum, where w(D)=Σe∈D w(e). In this paper, we show that the perfect (efficient) edge domination problem is NP-complete on bipartite (planar bipartite) graphs. Moreover, we present linear-time algorithms to solve the weighted perfect (efficient) edge domination problem on generalized series-parallel graphs and chordal graphs.