28122333
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
We examine the issue of mining association rules among items in a large database of sales transactions. Mining association rules means that, given a database of sales transactions, to discover all associations among items such that the presence of some items in a transaction will imply the presence of other items in the same transaction. The mining of association rules can be mapped into the problem of discovering large itemsets where a large itemset is a group of items that 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, these 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 sets in early iterations is usually the dominating factor for the overall data mining performance. To address this issue, we develop an effective algorithm for the candidate set generation. It is a hash-based algorithm and is especially effective for the generation of a candidate set for large 2-itemsets. 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. The advantage of the proposed algorithm also provides us the opportunity of reducing the amount of disk I/O required. An extensive simulation study is conducted to evaluate performance of the proposed algorithm.
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.
3768369
Efficient Data Mining for Path Traversal Patterns
The authors explore a new data mining capability that involves mining path traversal patterns in a distributed information-providing environment where documents or objects are linked together to facilitate interactive access. The solution procedure consists of two steps. First, they derive an algorithm to convert the original sequence of log data into a set of maximal forward references. By doing so, one can filter out the effect of some backward references, which are mainly made for ease of traveling and concentrate on mining meaningful user access sequences. Second, they derive algorithms to determine the frequent traversal patterns-i.e., large reference sequences-from the maximal forward references obtained. Two algorithms are devised for determining large reference sequences; one is based on some hashing and pruning techniques, and the other is further improved with the option of determining large reference sequences in batch so as to reduce the number of database scans required. Performance of these two methods is comparatively analyzed. It is shown that the option of selective scan is very advantageous and can lead to prominent performance improvement. Sensitivity analysis on various parameters is conducted.
17152003
WLAN Security: Current and Future
Wireless LAN deployment improves users' mobility, but it also brings a range of security issues that affect emerging standards and related technologies.
17114395
Efficient multicast stream authentication using erasure codes
We describe a novel method for authenticating multicast packets that is robust against packet loss. Our focus is to minimize the size of the communication overhead required to authenticate the packets. Our approach is to encode the hash values and the signatures with Rabin's Information Dispersal Algorithm (IDA) to construct an authentication scheme that amortizes a single signature operation over multiple packets. This strategy is especially efficient in terms of space overhead, because just the essential elements needed for authentication (i.e., one hash per packet and one signature per group of packets) are used in conjunction with an erasure code that is space optimal. Using asymptotic techniques, we derive the authentication probability of our scheme using two different bursty loss models. A lower bound of the authentication probability is also derived for one of the loss models. To evaluate the performance of our scheme, we compare our technique with four other previously proposed schemes using empirical results.
6379048
Secure Cookies on the Web
Web servers and browsers typically use cookies to capture information for subsequent communications, which provides continuity and state across HTTP connections. At present, Internet e-commerce is somewhat limited in using cookies because sensitive information cannot be securely stored and communicated in typical cookies. Secure cookies offer a potential solution to this problem.
52850595
LOD Canny Edge Based Boundary Edge Selection for Human Body Tracking
We propose a simple method for tracking a nonparameterized subject contour in a single video stream with a moving camera and changing background. Our method is based on level-of-detail (LOD) Canny edge maps and graph-based routing operations on the LOD maps. LOD Canny edge maps are generated by changing scale parameters for a given image. Simple (strong) Canny edge map has the smallest number of edge pixels while the most detailed Canny edge map, Wcanny N , has the biggest number of edge pixels. We start our basic tracking using strong Canny edges generated from large image intensity gradients of an input image, called Scanny edges to reduce side-effects because of irrelevant edges. Starting from Scanny edges, we get more edge pixels ranging from simple Canny edge maps until the most detailed Canny edge maps. LOD Canny edge pixels become nodes in routing, and LOD values of adjacent edge pixels determine routing costs between the nodes. We find a best route to follow Canny edge pixels favoring stronger Canny edge pixels. Our accurate tracking is based on reducing effects from irrelevant edges by selecting the stronger edge pixels, thereby relying on the current frame edge pixel as much as possible contrary to other approaches of always combining the previous contour. Our experimental results show that this tracking approach is robust enough to handle a complex-textured scene.
5761929
Dual-beam structured-light scanning for 3-D object modeling
In this paper we present our Dual-Beam Structured-Light Scanner (DSLS), a scanning system that generates range maps much richer than those obtained from a conventional structured-light scanning system. Range maps produced by DSLS require fewer registrations for 3-D modeling. We show that the DSLS system more easily satisfies what are often difficult-to-satisfy conditions for determining the 3-D coordinates of an arbitrary object point. Two specific advantages of DSLS over conventional structured-light scanning are: (1) A single scan by the DSLS system is capable of generating range data on more surfaces than possible with the conventional approach using the same number of camera images. And (2) since the data collected by DSLS is more free of self-occlusions, the object needs be examined from a smaller number of viewpoints.