7393635
On a decomposition for infinite transition matrices
Heyman gives an interesting factorization of I-P, where P is the transition probability matrix for an ergodic Markov chain. We show that this factorization is valid if and only if the Markov chain is recurrent. Moreover, we provide a decomposition result which includes all ergodic, null recurrent as well as the transient Markov chains as special cases. Such a decomposition has been shown to be useful in the analysis of queues.
7249461
Equilibrium probability calculations for a discrete-time bulk queue model
Many problems in management science and telecommunications can be solved by the analysis of aDX/Dm/1 queueing model. In this paper, we use the zeros, both inside and outside the unit circle, of the denominator of the generating function of the model to obtain an explicit closed-form solution for the equilibrium probabilities of the number of customers in the system. The moments of the number of customers in the queue or in the system are also studied. When there are infinitely many zeros outside the unit circle, we propose an approximation method using polynomials. This method yields correct values for a finite number of the probabilities, the number depending on the degree of the polynomial approximation.
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.
30853891
Comprehensive Verification Framework for Dependability of Self-optimizing Systems
By integrating formal specification and formal verification into the design phase of a system development process, the correctness of the system can be ensured to a great extent. However, it is not sufficient for a self-optimizing system that needs to exchange its components safely and consistently over time. Therefore, this paper presents a comprehensive verification framework to guarantee the dependability of such a self-optimizing system at the design phase (off-line verification) as well as at the runtime phase (on-line verification). The proposed verification framework adopts AsmL as intermediate representation for the system specification and on-the-fly model checking technique for alleviating the state space explosion problem. The off and the on -line verifications are performed at (RT-UML) model level. The properties to be checked are expressed by RT-OCL where the underlying temporal logic is restricted to time-annotated ACTL/LTL formulae. In particular, the on-line verification is achieved by running the on-the-fly model checking interleaved with the execution of the checked system in a pipelined manner.
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.
42961259
Authorship Attribution Via Combination of Evidence
Authorship attribution is a process of determining who wrote a particular document. We have found that different systems work well for particular sets of authors but not others. In this paper, we propose three authorship attribution systems, based on different ways of combining existing methodologies. All systems show better effectiveness than the state-of-art methods.
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.