37471711
Coarsening Classification Rules on Basis of Granular Computing
The task of classification, as a well-studied field of machine learning and data mining, has two main purposes: describing the classification rules in the training dataset and predicting the unseen new instances in the testing dataset. Normally, one expects a high accuracy for precisely descriptive use. However, a higher accuracy is intended to generate longer rules, which are not easy to understand, and may overfit the training instances. This motivates us to propose the approach of coarsening the classification rules, namely, reasonably sacrifice the accuracy of description in a controlled level in order to improve the comprehensibility and predictability. The framework of granular computing provides us a formal and systematic methodology for doing this. In this paper, a modified PRISM classification algorithm is presented based on the framework of granular computing.
14058950
A Profit-Based Business Model for Evaluating Rule Interestingness
Different types of rules are mined from transaction databases often with the goal of improving sales and services. In this paper, we link the interestingness of rules with the context of business marketing. We consider the profits generated from some specific marketing strategies that are developed based on particular discovered rules. This leads to a profit-based business model for evaluating rule interestingness. With this additional utility, we investigate some relationships between different marketing strategies and fundamental properties of rules for profit increasing.
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.
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.
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
7856172
Explanation Oriented Association Mining Using Rough Set Theory
This paper presents a new philosophical view and methodology for data mining. A framework of explanation oriented data mining is proposed and studied with respect to association mining. The notion of conditional associations is adopted, which explicitly expresses the conditions under which an association occurs. To illustrate the basic ideas, the theory of rough sets is used to construct explanations.
14081236
Potential Advantages of Semantic Web for Internet Commerce
Past decade saw much hype in the area of information technology. The emerging of semantic Web makes us ask if it is another hype. This paper focuses on its potential application in Internet commerc ...
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.
184418
Self-adaptive clock synchronization for computational grid
This paper presents an innovative method to synchronize physical clocks for a computational grid, in particular for a computational grid linked through the asynchronous Intranet or Internet environments. The method discussed is an asynchronous self-adaptive clock synchronization mechanism. Two strategies for clock synchronisationare introduced. (1), Use continuous time intervals to calculate the precision of clocks, which can reduce the effect of network delay efficiently. (2) Every node synchronizes its clock, with its leader actively. In addition, a node self-adaptive model is presented, and the relationship between the clock precision and synchronization time is induced, hence a node can predict when it should begin the synchronization process. Detailed simulation and extension of this issue are provided at the end of the paper. The presented model is both practical and feasible.
17699590
Adapting K-Means Algorithm for Discovering Clusters in Subspaces
Subspace clustering is a challenging task in the field of data mining. Traditional distance measures fail to differentiate the furthest point from the nearest point in very high dimensional data space. To tackle the problem, we design minimal subspace distance which measures the similarity between two points in the subspace where they are nearest to each other. It can discover subspace clusters implicitly when measuring the similarities between points. We use the new similarity measure to improve traditional k-means algorithm for discovering clusters in subspaces. By clustering with low-dimensional minimal subspace distance first, the clusters in low-dimensional subspaces are detected. Then by gradually increasing the dimension of minimal subspace distance, the clusters get refined in higher dimensional subspaces. Our experiments on both synthetic data and real data show the effectiveness of the proposed similarity measure and algorithm.
8188664
Scalable On-Demand Streaming of Nonlinear Media
A conventional video file contains a single temporally-ordered sequence of video frames. Clients requesting on-demand streaming of such a file receive (all or intervals of) the same content. For popular files that receive many requests during a file playback time, scalable streaming protocols based on multicast or broadcast have been devised. Such protocols require server and network bandwidth that grow much slower than linearly with the file request rate. This paper considers ldquononlinearrdquo video content in which there are parallel sequences of frames. Clients dynamically select which branch of the video they wish to follow, sufficiently ahead of each branch point so as to allow the video to be delivered without jitter. An example might be ldquochoose-your-own-endingrdquo movies. With traditional scalable delivery architectures such as movie theaters or TV broadcasting, such personalization of the delivered video content is very difficult or impossible. It becomes feasible, in principle at least, when the video is streamed to individual clients over a network. For on-demand streaming of nonlinear media, this paper analyzes the minimal server bandwidth requirements, and proposes and evaluates practical scalable delivery protocols.