46761900
Mining weak rules
Finding patterns from data sets is a fundamental task of data mining. If we categorize all patterns into strong, weak, and random, conventional data mining techniques are designed only to find strong patterns, which hold for numerous objects and are usually consistent with the expectations of experts. We address the problem of finding weak patterns (i.e., reliable exceptions) from databases. They are valid for a small number of objects. A simple approach is proposed which uses deviation analysis to identify interesting exceptions and explore reliable ones. It is also flexible in handling both subjective and objective exceptions. We demonstrate the effectiveness of the proposed approach through a benchmark data set.
5365583
NeuroRule: A Connectionist Approach to Data Mining
Classification, which involves finding rules that partition a given da.ta set into disjoint groups, is one class of data mining problems. Approaches proposed so far for mining classification rules for large databases are mainly decision tree based symbolic learning methods. The connectionist approach based on neura.l networks has been thought not well suited for data mining. One of the major reasons cited is that knowledge generated by neural networks is not explicitly represented in the form of rules suitable for verification or interpretation by humans. This paper examines this issue. With our newly developed algorithms, rules which are similar to, or more concise than those generated by the symbolic methods can be extracted from the neural networks. The data mining process using neural networks with the emphasis on rule extraction is described. ExperimenM results and comparison with previously published works are presented.
8367210
Sensor Sequence Modeling for Driving
Intelligent systems in automobiles need to be aware of the driving and driver context. Available sensor data stream has to be modeled and monitored in order to do so. Currently there exist no building blocks for hierarchical modeling of driving. By semi-supervised segmentation such building blocks can be discovered. We call them drivemes in analogy to phonemes. More parsimonious modeling of driving becomes now possible.
46794790
Analysis of Hidden Representations by Greedy Clustering
The hidden layer of backpropagation neural networks (NNs) holds the key to the networks' success in solving pattern classification problems. The units in the hidden layer encapsulate the network's internal representations of the outside world described by the input data. this paper, the hidden representations of trained networks are investigated by means simple greedy clustering algorithm. This clustering algorithm is applied to networks have been trained to solve well-known problems: the monks problems, the 5-bit problem and the contiguity problem. The results from applying the algorithm to problems with known concepts provide us with a better understanding of NN learning. These also explain why NNs achieve higher predictive accuracy than that of decision-tree methods. The results of this study can be readily applied to rule extraction from Production rules are extracted for the parity and the monks problems, as well as benchmark data set: Pima Indian diabetes diagnosis. The extracted rules from the Indi...
16386059
A Monotonic Measure for Optimal Feature Selection
Feature selection is a problem of choosing a subset of relevant features. In general, only exhaustive search can bring about the optimal subset. With a monotonic measure, exhaustive search can be avoided without sacrificing optimality. Unfortunately, most error- or distancebased measures are not monotonic. A new measure is employed in this work that is monotonic and fast to compute. The search for relevant features according to this measure is guaranteed to be complete but not exhaustive. Experiments are conducted for verification.
14543078
Resource description framework: metadata and its applications
Universality, the property of the Web that makes it the largest data and information source in the world, is also the property behind the lack of a uniform organization scheme that would allow easy access to data and information. A semantic web, wherein different applications and Web sites can exchange information and hence exploit Web data and information to their full potential, requires the information about Web resources to be represented in a detailed and structured manner. Resource Description Framework (RDF), an effort in this direction supported by the World Wide Web Consortium, provides a means for the description of metadata which is a necessity for the next generation of interoperable Web applications. The success of RDF and the semantic web will depend on (1) the development of applications that prove the applicability of the concept, (2) the availability of application interfaces which enable the development of such applications, and (3) databases and inference systems that exploit RDF to identify and locate most relevant Web resources. In addition, many practical issues, such as security, ease of use, and compatibility, will be crucial in the success of RDF. This survey aims at providing a glimpse at the past, present, and future of this upcoming technology and highlights why we believe that the next generation of the Web will be more organized, informative, searchable, accessible, and, most importantly, useful. It is expected that knowledge discovery and data mining can benefit from RDF and the Semantic Web.
1363024
Java Program Verification via a JVM Deep Embedding in ACL2
In this paper, we show that one can “deep-embed” the Java bytecode language, a fairly complicated language with a rich semantics, into the first order logic of ACL2 by modeling a realistic JVM. We show that with proper support from a semi-automatic theorem prover in that logic, one can reason about the correctness of Java programs. This reasoning can be done in a direct and intuitive way without incurring the extra burden that has often been associated with hand proofs, or proofs that make use of less automated proof assistance. We present proofs for two simple Java programs as a showcase.
14767737
Dynamic co-scheduling of distributed computation and replication
We are interested in developing the infrastructural tools that allow a distributed data intensive computing environment to be shared by a group of collaborating but geographically separated researchers in an interactive manner, as opposed to a batch mode of operation. However, without advanced reservation, it is difficult to assure a certain level of performance on a large number of shared and heterogeneous servers. To achieve scalable parallel speedups in this scenario, we must closely integrate the management of computation and runtime data movement. In this paper, we first define the canonical scheduling problem for datasets distributed with k-way replication in the wide area. We then develop a dynamic co-scheduling algorithm that integrates the scheduling of computation and data movement. Using time-varying visualization as the driving application, we demonstrate that our co-scheduling approach improves not only application performance but also server utilization at a very reasonable cost.
35256277
Generalized shuffle-exchange digraphs: Hamiltonian properties
The k-ary n-dimensional shuffle-exchange directed network S(k, n) consists of k/sup n/ nodes such that each node is represented as an k-ary n-tuple vector, a/sub 1/a/sub 2/...a/sub n/, where a/sub i/ is in [0, k-1]. Node a/sub 1/a/sub 2/...a/sub n/ is adjacent to node a/sub 2/a/sub 3/...a/sub n/a/sub 1/ (one shuffle link) and k-1 other nodes a/sub 1/a/sub 2/...a/sub n-1/b, where b/spl isin/[0, k-1] and b/spl ne/a/sub n/ (k-1 exchange links). S(k, n) have been widely used as topologies for VLSI networks, parallel architectures, and communication systems. However, since S(k, n) does not exist for the number of nodes between k/sup n/ and k/sup n+1/, Liu and Hsu have recently proposed a class of digraphs GS(k, N), which generalized S(k, n) to any number N of nodes. They also showed that GS(k, N) retains all the nice properties of S(k, n). In this paper, we survey these combinatorial properties and study Hamiltonian properties of GS(k, N). In particular, we have successfully obtained a Hamiltonian circuit for GS(k, k(k+1)) for any k>2.
16336933
Adaptive Video Watermarking Scheme
In this paper, a new adaptive watermarking scheme for uncompressed and compressed video is presented. The proposed method embeds the watermark in DC components by quantizing DC coefficients to reference values, instead of embedding the watermark in AC components. To increase robustness and perceptual invisibility, adaptive quantization control strategy (AQCS) and error compensation strategy (ECS) are introduced. Experimental results demonstrate the proposed watermarking scheme is robust and invisible, and is feasible for real time system implementation.
17120587
Combination methods in microarray analysis
Microarray technology and experiment can produce thousands or tens of thousands of gene expression measurements in a single cellular mRNA sample. Selecting a list of informative differential genes from these measurement data has been the central problem for microarray analysis. Many methods to identify informative genes have been proposed in the past. However, due to the complexity of biological systems, each proposed method seems to perform nicely in a particular data set or specific experiment. It remains a great challenge to come up with a selection method for a wider spectrum of experiments and a broader variety of data sets. In this paper, we take the approach of method combination using data fusion and rank-score graph which have been used successfully in other application domains such as information retrieval, pattern recognition and tracking, and molecular similarity search. Our method combination is efficient and flexible and can be extended to become a general learning system for microarray gene expression analysis.