16088118
An effective hash-based algorithm for mining association rules
In this paper, we examine the issue of mining association rules among items in a large database of sales transactions. The mining of association rules can be mapped into the problem of discovering large itemsets where a large itemset is a group of items which appear in a sufficient number of transactions. The problem of discovering large itemsets can be solved by constructing a candidate set of itemsets first and then, identifying, within this candidate set, those itemsets that meet the large itemset requirement. Generally this is done iteratively for each large k-itemset in increasing order of k where a large k-itemset is a large itemset with k items. To determine large itemsets from a huge number of candidate large itemsets in early iterations is usually the dominating factor for the overall data mining performance. To address this issue, we propose an effective hash-based algorithm for the candidate set generation. Explicitly, the number of candidate 2-itemsets generated by the proposed algorithm is, in orders of magnitude, smaller than that by previous methods, thus resolving the performance bottleneck. Note that the generation of smaller candidate sets enables us to effectively trim the transaction database size at a much earlier stage of the iterations, thereby reducing the computational cost for later iterations significantly. Extensive simulation study is conducted to evaluate performance of the proposed algorithm.
16144421
Fast algorithms for projected clustering
The clustering problem is well known in the database literature for its numerous applications in problems such as customer segmentation, classification and trend analysis. Unfortunately, all known algorithms tend to break down in high dimensional spaces because of the inherent sparsity of the points. In such high dimensional spaces not all dimensions may be relevant to a given cluster. One way of handling this is to pick the closely correlated dimensions and find clusters in the corresponding subspace. Traditional feature selection algorithms attempt to achieve this. The weakness of this approach is that in typical high dimensional data mining applications different sets of points may cluster better for different subsets of dimensions. The number of dimensions in each such cluster-specific subspace may also vary. Hence, it may be impossible to find a single small subset of dimensions for all the clusters. We therefore discuss a generalization of the clustering problem, referred to as the projected clustering problem, in which the subsets of dimensions selected are specific to the clusters themselves. We develop an algorithmic framework for solving the projected clustering problem, and test its performance on synthetic data.
12592675
A New Approach for Active Stereo Camera Calibration
By active stereo we mean a stereo vision system that allows for independent panning and tilting for each of the two cameras. One advantage of active stereo in relation to regular stereo is the former's wider effective field of view; if an object is too close to the camera baseline, the depth to the object can still be estimated accurately by panning the cameras appropriately. Another advantage of active stereo is that it can yield a larger number of depth measurements simultaneously for each position of the platform on which the camera system is mounted. Panning and tilting over a large angular range, while being the main reason for the advantages of active stereo, also make it more challenging to calibrate such systems. For a calibration procedure to be effective for active stereo, the estimated parameters must be valid over the entire range of the pan and tilt angles. This paper presents a new approach to the calibration of such vision systems. Our method is based on the rationale that an active stereo calibration procedure must explicitly estimate the locations and the orientations of the pan and tilt rotating axes for the cameras through a closed-form solution. When these estimates for the axes are combined with the homogeneous transform relationships that link the various coordinate frames, we end with a calibration that is valid over a large variation in the pan and tilt angles.
17981428
SECK: survivable and efficient clustered keying for wireless sensor networks
A wireless sensor network (WSN) typically consists of a large number of small sensor nodes and one or more high-end control and data aggregation nodes. Sensor nodes have limited computation and communication capabilities, and communicate via wireless links. Consequently, WSNs are highly vulnerable to attacks. This vulnerability is exacerbated when WSNs have to operate unattended in a hostile environment, such as battlefields. In this paper, we propose a novel self-organizing key management scheme for large-scale WSNs, called Sunivable and efficient clustered keying (SECK). Our scheme was designed specifically to address the key management issues within the low-tier of a hierarchical network architecture. Previous approaches for WSN key management adequately addressed operational issues, but to a large extent, ignored robustness and recoverability issues. Using simulation and analysis, we show that SECK is highly robust against key and node captures, and has noteworthy advantages over other key management schemes.
5714667
Extending software communications architecture for QoS support in SDR signal processing
The software communications architecture (SCA) is middleware for providing component interfaces and dynamic reconfigurability to software define radio (SDR) modems and it is widely adopted by the SDR forum. Although its main application domains include real-time signal processing, SCA lacks QoS support. We thus propose a QoS-enabled extension of SCA called Q-SCA. Our extended architecture incorporates a new waveform model, extended domain profiles that describe QoS requirements, and a modified application instantiation process that supports both admission control and resource allocation. We show the viability of Q-SCA through full implementation and experiments.
15361115
The UCONABC usage control model
In this paper, we introduce the family of UCONABC models for usage control (UCON), which integrate Authorizations (A), oBligations (B), and Conditions (C). We call these core models because they address the essence of UCON, leaving administration, delegation, and other important but second-order issues for later work. The term usage control is a generalization of access control to cover authorizations, obligations, conditions, continuity (ongoing controls), and mutability. Traditionally, access control has dealt only with authorization decisions on users' access to target resources. Obligations are requirements that have to be fulfilled by obligation subjects for allowing access. Conditions are subject and object independent environmental or system requirements that have to be satisfied for access. In today's highly dynamic, distributed environment, obligations and conditions are also crucial decision factors for richer and finer controls on usage of digital resources. Although they have been discussed occasionally in recent literature, most authors have been motivated from specific target problems and thereby limited in their approaches. The UCONABC model integrates these diverse concepts in a unified framework. Traditional authorization decisions are generally made at the time of requests but hardly recognize ongoing controls for relatively long-lived access or for immediate revocation. Moreover, mutability issues that deal with updates on related subject or object attributes as a consequence of access have not been systematically studied.Unlike other studies that have targeted on specific problems or issues, the UCONABC model seeks to enrich and refine the access control discipline in its definition and scope. UCONABC covers traditional access controls such as mandatory, discretionary, and role-based access control. Digital rights management and other modern access controls are also covered. UCONABC lays the foundation for next generation access controls that are required for today's real-world information and systems security. This paper articulates the core of this new area of UCON and develops several detailed models.
26579916
A Monge Property for the D-dimensional Transportation Problem
Abstract In 1963, Hoffman gave necessary and sufficient conditions under which a family of O( mn )-time greedy algorithms solves the classical two-dimensional transportation problem with m sources and n sinks. One member of this family, an algorithm based on the “northwest corner rule”, is of particular interest, as its running time is easily reduced to O(m + n). When restricted to this algorithm, Hoffman's result can be expressed as follows: the northwest-corner-rule greedy algorithm solves the two-dimensional transportation problem for all source and supply vectors if and only if the problem's cost array C = { c [ i , j ]} possesses what is known as the (two-dimensional) Monge property, which requires c [ i 1 , j 1 ] + c [ i 2 , j 2 ] ⩽ c [ i 1 , j 2 ] + c [ i 2 , j 1 ] for i 1 i 2 and j 1 j 2 . This paper generalizes this last result to a higher dimensional variant of the transportation problem. We show that the natural extension of the northwest-corner-rule greedy algorithm solves an instance of the d -dimensional transportation problem if and only if the problem's cost array possesses a d -dimensional Monge property recently proposed by Aggarwal and Park in the context of their study of monotone arrays. We also give several new examples of cost arrays with this d -dimensional Monge property.