29045473
On Reduct Construction Algorithms
This paper critically analyzes reduct construction methods at two levels. At a high level, one can abstract commonalities from the existing algorithms, and classify them into three basic groups based on the underlying control structures. At a low level, by adopting different heuristics or fitness functions for attribute selection, one is able to derive most of the existing algorithms. The analysis brings new insights into the problem of reduct construction, and provides guidelines for the design of new algorithms
6912630
A Model of Machine Learning Based on User Preference of Attributes
A formal model of machine learning by considering user preference of attributes is proposed in this paper. The model seamlessly combines internal information and external information. This model can be extended to user preference of attribute sets. By using the user preference of attribute sets, user preferred reducts can be constructed.
5309798
Conflict Analysis Based on Discernibility and Indiscernibility
The dual notions of discernibility and indiscernibility play an important role in intelligent data analysis. While discernibility focuses on the differences, the indiscernibility reveals the similarities. By considering them together in a same framework, one is able to obtain new insight of data. The main objective of the paper is to apply discernibility and indiscernibility to conflict analysis, a theory dealing with opinions of a set of agents on a set of issues. In particular, we are interested in the problem of issue reduction, so that a reduced set of issues can be obtained without loss of crucial information of the original set of issues. Extending the results from rough set theory, three types of issue reducts are introduced. They correspond to discernibility, indiscernibility, and discernibility-and-indiscernibility reducts, respectively. The results of this paper may offer a new research direction in rough set analysis in general, and conflict analysis in particular.
9438100
Classification Based on Logical Concept Analysis
This paper studies the problem of classification by using a concept lattice as a search space of classification rules. The left hand side of a classification rule is composed by a concept, including its extension and its intension, and the right hand side is the class label that the concept implies. Particularly, we show that logical concepts of the given universe are naturally associated with any consistent classification rules generated by any partition-based or covering-based algorithm, and can be characterized as a special set of consistent classification rules. An algorithm is proposed to find a set of the most general consistent concepts.
1134220
The design of an interactive online help desk in the Alexandria Digital Library
In large software systems such as digital libraries, electronic commerce applications, and customer support systems, the user interface and system are often complex and difficult to navigate. It is necessary to provide users with interactive online support to help users learn how to effectively use these applications. Such online help facilities can include providing tutorials and animated demonstrations, synchronized activities between users and system supporting staff for real time instruction and guidance, multimedia communication with support staff such as chat, voice, and shared whiteboards, and tools for quick identification of user problems. In this paper, we investigate how such interactive online help support can be developed and provided in the context of a working system, the Alexandria Digital Library (ADL) for geospatially-referenced data. We developed an online help system, AlexHelp!. AlexHelp! supports collaborative sessions between the user and the librarian (support staff) that include activities such as map browsing and region selection, recorded demonstration sessions for the user, primitive tools for analyzing user sessions, and channels for voice and text based communications. The design of AlexHelp! is based on user activity logs, and the system is a light-weight software component that can be easily integrated into the ADL user interface client. A prototype of AlexHelp! is developed and integrated into the ADL client; both the ADL client and AlexHelp! are written in Java.
8299246
HyLog: A High Performance Approach to Managing Disk Layout
Our objective is to improve disk I/O performance in multi-disk systems supporting multiple concurrent users, such as file servers, database servers, and email servers. In such systems, many disk reads are absorbed by large in-memory buffers, and so disk writes comprise a large portion of the disk I/O traffic. LFS (Log-structured File System) has the potential to achieve superior write performance by accumulating small writes into large blocks and writing them to new places, rather than overwriting on top of their old copies (called Overwrite). Although it is commonly believed that the high segment cleaning overhead of LFS makes it a poor choice for workloads with random updates, in this paper we find that because of the fast improvement of disk technologies, LFS significantly outperforms Overwrite in a wide range of system configurations and workloads (including the random update workload) under modern and future disks. 
 
LFS performs worse than Overwrite, however, when the disk space utilization is very high due to the high cleaning cost. In this paper, we propose a new approach, the Hybrid Log-structured (Hy-Log) disk layout, to overcome this problem. Hy-Log uses a log-structured approach for hot pages to achieve high write performance, and Overwrite for cold pages to reduce the cleaning cost. We compare the performance of HyLog to that of Overwrite, LFS and WOLF (the latest improvement on LFS) under various system configurations and workloads. Our results show that, in most cases, Hylog performs comparably to the best of the other three approaches.
6467988
Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering
This paper evaluates the performance of different criterion functions in the context of partitional clustering algorithms for document datasets. Our study involves a total of seven different criterion functions, three of which are introduced in this paper and four that have been proposed in the past. We present a comprehensive experimental evaluation involving 15 different datasets, as well as an analysis of the characteristics of the various criterion functions and their effect on the clusters they produce. Our experimental results show that there are a set of criterion functions that consistently outperform the rest, and that some of the newly proposed criterion functions lead to the best overall results. Our theoretical analysis shows that the relative performance of the criterion functions depends on (i) the degree to which they can correctly operate when the clusters are of different tightness, and (ii) the degree to which they can lead to reasonably balanced clusters.
15518950
Effective and Scalable Authorship Attribution Using Function Words
Techniques for identifying the author of an unattributed document can be applied to problems in information analysis and in academic scholarship. A range of methods have been proposed in the research literature, using a variety of features and machine learning approaches, but the methods have been tested on very different data and the results cannot be compared. It is not even clear whether the differences in performance are due to feature selection or other variables. In this paper we examine the use of a large publicly available collection of newswire articles as a benchmark for comparing authorship attribution methods. To demonstrate the value of having a benchmark, we experimentally compare several recent feature-based techniques for authorship attribution, and test how well these methods perform as the volume of data is increased. We show that the benchmark is able to clearly distinguish between different approaches, and that the scalability of the best methods based on using function words features is acceptable, with only moderate decline as the difficulty of the problem is increased.
32435431
Regularized Semi-supervised Classification on Manifold
Semi-supervised learning gets estimated marginal distribution PX with a large number of unlabeled examples and then constrains the conditional probability p(y | x) with a few labeled examples. In this paper, we focus on a regularization approach for semi-supervised classification. The label information graph is first defined to keep the pairwise label relationship and can be incorporated with neighborhood graph which reflects the intrinsic geometry structure of PX. Then we propose a novel regularized semi-supervised classification algorithm, in which the regularization term is based on the modified Graph Laplacian. By redefining the Graph Laplacian, we can adjust and optimize the decision boundary using the labeled examples. The new algorithm combines the benefits of both unsupervised and supervised learning and can use unlabeled and labeled examples effectively. Encouraging experimental results are presented on both synthetic and real world datasets.