750246
Noise Tolerance of EP-Based Classifiers
Emerging Pattern (EP)-based classifiers are a type of new classifiers based on itemsets whose occurrence in one dataset varies significantly from that of another. These classifiers are very promising and have shown to perform comparably with some popular classifiers. In this paper, we conduct two experiments to study the noise tolerance of EP-based classifiers. A primary concern is to ascertain if overfitting occurs in them. Our results highlight the fact that the aggregating approach in constructing EP-based classifiers prevents them from overfitting. We further conclude that perfect training accuracy does not necessarily lead to overfitting of a classifier as long as there exists a suitable mechanism, such as an aggregating approach, to counterbalance any propensity to overfit.
17533834
Efficiently Computing the Top N Averages in Iceberg Cubes
Data cubes are an enabling approach for efficient on-line analytical processing (OLAP) systems. Iceberg cubes are special cubes consisting of the aggregates of multi-dimensional groups that satisfy user-specified thresholds. When there are a relatively large number of dimensions, the number of groups in an iceberg cube is huge. End users cannot fully understand the aggregate results or directly use them to make a decision. Approaches for the efficient computation of the top n groups have been proposed and have been shown to work well on iceberg cubes with simple aggregate functions such as COUNT and SUM. In this paper, we study the efficient computation of the top n groups with the complex aggregate function AVERAGE. As the average of a sub-group does not increase/decrease monotonically with its super-group, AVERAGE constraints cannot be used directly for pruning. We propose a new technique upper-bounding average which is anti-monotonic and can be used for low-cost effective pruning. Based on a tree structure representing groups, search and pruning techniques are developed, and an algorithm is proposed to compute the top n averages efficiently.
29730920
Translating update operations from relational to object-oriented databases
Abstract In migrating a legacy relational database system to the object-oriented (OO) platform, when database migration completes, application modules are to be migrated, where embedded relational database operations are mapped into their OO correspondents. In this paper we study mapping relational update operations to their OO equivalents, which include UPDATE 1 , INSERT and DELETE operations. Relational update operation translation from relational to OO faces the touchy problem of transformation from a value-based relationship model to a reference-based model and maintaining the relational integrity constraints. Moreover, with a relational database where inheritance is expressed as attribute value subset relationship, changing of some attribute values may lead to the change of the position of an object in the class inheritance hierarchy, which we call object migration. Considering all these aspects, algorithms are given mapping relational UPDATE, INSERT and DELETE operations to their OO correspondents. Our work emphasize in examining the differences in the representation of the source schema's semantics resulting from the translation process, as well as differences in the inherent semantics of the two models.
8448018
Efficient Frequent Pattern Mining on Web Logs
Mining frequent patterns from Web logs is an important data mining task. Candidate-generation-and-test and pattern-growth are two representative frequent pattern mining approaches. We have conducted extensive experiments on real world Web log data to analyse the characteristics of Web logs and the behaviours of these two approaches on Web logs. To improve the performance of current algorithms on mining Web logs, we propose a new algorithm – Combined Frequent Pattern Mining (CFPM) to cater for Web log data specifically. We use heuristics to prune search space and reduce costs in mining so that better efficiency is achieved. Experimental results show that CFPM significantly improves the performance of the pattern-growth approach by 1.2–7.8 times on mining frequent patterns from Web logs.
8280671
Information-Based Classification by Aggregating Emerging Patterns
Emerging patterns (EPs) are knowledge patterns capturing contrasts between data classes. In this paper, we propose an information-based approach for classification by aggregating emerging patterns. The constraint-based EP mining algorithm enables the system to learn from large-volume and high-dimensional data; the new approach for selecting representative EPs and efficient algorithm for finding the EPs renders the system high predictive accuracy and short classification time. Experiments on many benchmark datasets show that the resulting classifiers have good overall predictive accuracy, and are often also superior to other state-of-the-art classification systems such as C4.5, CBA and LB.
717712
Efficient Computation of Iceberg Cubes by Bounding Aggregate Functions
The iceberg cubing problem is to compute the multidimensional group-by partitions that satisfy given aggregation constraints. Pruning unproductive computation for iceberg cubing when nonantimonotone constraints are present is a great challenge because the aggregate functions do not increase or decrease monotonically along the subset relationship between partitions. In this paper, we propose a novel bound prune cubing (BP-Cubing) approach for iceberg cubing with nonantimonotone aggregation constraints. Given a cube over n dimensions, an aggregate for any group-by partition can be computed from aggregates for the most specific n--dimensional partitions (MSPs). The largest and smallest aggregate values computed this way become the bounds for all partitions in the cube. We provide efficient methods to compute tight bounds for base aggregate functions and, more interestingly, arithmetic expressions thereof, from bounds of aggregates over the MSPs. Our methods produce tighter bounds than those obtained by previous approaches. We present iceberg cubing algorithms that combine bounding with efficient aggregation strategies. Our experiments on real-world and artificial benchmark data sets demonstrate that BP-Cubing algorithms achieve more effective pruning and are several times faster than state-of-the-art iceberg cubing algorithms and that BP-Cubing achieves the best performance with the top-down cubing approach.
28741459
Learning Layouts of Biological Datasets Semi-automatically
A key challenge associated with the existing approaches for data integration and workflow creation for bioinformatics is the effort required to integrate a new data source. As new data sources emerge, and data formats and contents of existing data sources evolve, wrapper programs need to be written or modified. This can be extremely time consuming, tedious, and error-prone. 
 
This paper describes our semi-automatic approach for learning the layout of a flat-file bioinformatics dataset. Our approach involves three key steps. The first step is to use a number of heuristics to infer the delimiters used in the program. Specifically, we have developed a metric that uses information on the frequency and starting position of sequences. Based on this metric, we are able to find a superset of delimiters, and then we can seek user input to eliminate the incorrect ones. Our second step involves generating a layout descriptor based on the relative order in which the delimiters occur. Our final step is to generate a parser based on the layout descriptor. Our heuristics for finding the delimiters has been evaluated using three popular flat-file biological datasets.
10570847
CLOCK-Pro: An Effective Improvement of the CLOCK Replacement
With the ever-growing performance gap between memory systems and disks, and rapidly improving CPU performance, virtual memory (VM) management becomes increasingly important for overall system performance. However, one of its critical components, the page replacement policy, is still dominated by CLOCK, a replacement policy developed almost 40 years ago. While pure LRU has an unaffordable cost in VM, CLOCK simulates the LRU replacement algorithm with a low cost acceptable in VM management. Over the last three decades, the inability of LRU as well as CLOCK to handle weak locality accesses has become increasingly serious, and an effective fix becomes increasingly desirable. 
 
Inspired by our I/O buffer cache replacement algorithm, LIRS [13], we propose an improved CLOCK replacement policy, called CLOCK-Pro. By additionally keeping track of a limited number of replaced pages, CLOCK-Pro works in a similar fashion as CLOCK with a VM-affordable cost. Furthermore, it brings all the much-needed performance advantages from LIRS into CLOCK. Measurements from an implementation of CLOCK-Pro in Linux Kernel 2.4.21 show that the execution times of some commonly used programs can be reduced by up to 47%.
207738295
Kernel Nearest-Neighbor Algorithm
The ‘kernel approach’ has attracted great attention with the development of support vector machine (SVM) and has been studied in a general way. It offers an alternative solution to increase the computational power of linear learning machines by mapping data into a high dimensional feature space. This ‘approach’ is extended to the well-known nearest-neighbor algorithm in this paper. It can be realized by substitution of a kernel distance metric for the original one in Hilbert space, and the corresponding algorithm is called kernel nearest-neighbor algorithm. Three data sets, an artificial data set, BUPA liver disorders database and USPS database, were used for testing. Kernel nearest-neighbor algorithm was compared with conventional nearest-neighbor algorithm and SVM Experiments show that kernel nearest-neighbor algorithm is more powerful than conventional nearest-neighbor algorithm, and it can compete with SVM.
5541899
Automatic identification of digital video based on shot-level sequence matching
To locate a video clip in large collections is very important for retrieval applications, especially for digital rights management. In this paper, we present a novel technique for automatic identification of digital video. This new algorithm is based on dynamic programming that fully uses the temporal dimension to measure the similarity between two video sequences. A normalized chromaticity histogram is used as a feature which is illumination-invariant. Dynamic programming is applied on shot-level to find the optimal nonlinear mapping between video sequences. Two new normalized distance measures are presented for video sequence matching. One measure is based on the normalization of the optimal path found by dynamic programming. The other measure combines both the visual features and the temporal information. Experimental results show that the shot-level approach is robust to frame rate conversion, color correction, and compressions. The proposed distance measures are suitable for variable-length comparisons.
44391380
Bi-directional gradient labeling and registration for gray-scale image segmentation
Watershed is one of the commonly used methods for image segmentation. In this paper, we introduce a new segmentation scheme based on bi-directional labeling and registration and prove that its segmentation performance is equivalent to that of conventional watershed but it is much more computational efficient The bi-directional labeling and registration scheme, which will be referred to as BIDS, involves only linear scans of image pixels. It uses one dimensional operations instead of queues while traditional segmentation algorithms are two dimensional problems. BIDS also provides unique labels for each homogeneous regions. In addition to achieving the same segmentation results as conventional watershed, BIDS is four times less computationally complex than the conventional watersheds by immersion.