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.
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.
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.
11721850
Estimating LOC for information systems from their conceptual data models
Effort and cost estimation is crucial in software management. Estimation of software size plays a key role in the estimation. Line of Code (LOC) is still a commonly used software size measure. Despite the fact that software sizing is well recognized as an important problem for more than two decades, there is still much problem in existing methods. Conceptual data model is widely used in the requirements analysis for information systems. It is also not difficult to construct conceptual data models in the early stage of developing information systems. Much characteristic of an information system is actually reflected from its conceptual data model. We explore into the use of conceptual data model for estimating LOC. This paper proposes a novel method for estimating LOC for an information system from its conceptual data model through the use of multiple linear regression model. We have validated the method through collecting samples from both the industry and open-source systems.
12919460
Decay rate for a PH/M/2 queue with shortest queue discipline
In this paper, we consider a PH/M/2 queue in which each server has its own queue and arriving customers join the shortest queue. For this model, it has been conjectured that the decay rate of the tail probabilities for the shortest queue length in the steady state is equal to the square of the decay rate for the queue length in the corresponding PH/M/2 model with a single queue. We prove this fact in the sense that the tail probabilities are asymptotically geometric when the difference of the queue sizes and the arrival phase are fixed. Our proof is based on the matrix analytic approach pioneered by Neuts and recent results on the decay rates.
31784785
Tiger: Educational Research in the Age of the Internet
15790608
Clustering High-Dimensional Data with Low-Order Neighbors
Density-based and grid-based clustering are two main clustering approaches. The former is famous for its capability of discovering clusters of various shapes and eliminating noises, while the latter is well known for its high speed. Combination of the two approaches seems to provide better clustering results. To the best of our knowledge, however, all existing algorithms that combine density-based clustering and grid-based clustering take cells as atomic units, in the sense that either all objects in a cell belong to a cluster or no object in the cell belong to any cluster. This requires the cells to be small enough to ensure the fine resolution of results. In high-dimensional spaces, however, the number of cells can be very large when cells are small, which would make the clustering process extremely costly. On the other hand, the number of neighbors of a cell grows exponentially with the dimensionality of datasets, which makes the complexity increase further. In this paper, we present a new approach that takes objects (or points) as the atomic units, so that the restriction of cell size can be relaxed without degrading the resolution of clustering results. In addition, a concept of ith-order neighbors is introduced to avoid considering the exponential number of neighboring cells. By considering only low-order neighbors, our algorithm is very efficient while losing only a little bit of accuracy. Experiments on synthetic and public data show that our algorithm can cluster high-dimensional data effectively and efficiently.