Karl Menger's contributions to social thought

The work of Karl Menger in social science is briefly surveyed, in particular in the areas of marginal utility and diminishing value, utility, and uncertainty, a logic of imperatives based on deterrence, and a theory of voluntary associations in which cohesive groups are studied combinatorially.     

A survey of multicriteria optimization or the vector maximum problem,part I: 1776–1960

In this survey, the history of the subject from 1776 until 1960 is presented. A brief biographical sketch of Vilfredo Pareto is given first. Then, the more or less simultaneous development of the concepts of utility, preference, and welfare theory follows, with results which go back to Hausdorff and Cantor. A brief discussion of the work of Borel and von Neumann as initiators of game theory is included. Each of these areas has developed enough to warrant its own survey; hence, they are reviewed here only insofar as they provide necessary foundations. Thereafter, the concepts of efficiency, vector maximum problem, and Pareto optimality are reviewed in connection with production theory, programming, and economics. The survey is presented within a unified mathematical framework, and the emphasis is on mathematical results, rather than psychological or socio-economic discussion. To enable the reader to draw conclusions without having to obtain each article himself, the results have been presented in somewhat more detail than usual.     

Linear low-rank approximation and nonlinear dimensionality reduction

We present our recent work on both linear and nonlinear data reduction methods and algorithms: for the linear case we discuss results on structure analysis of SVD of column-partitioned matrices and sparse low-rank approximation; for the nonlinear case we investigate methods for nonlinear dimensionality reduction and manifold learning. The problems we address have attracted great deal of interest in data mining and machine learning.     

Using Data Mining to Find Patterns in Ant Colony Algorithm Solutions to the Travelling Salesman Problem

Travelling Salesman Problem （TSP） is a classical optimization problem and it is one of a class of NP- Problem. The purposes of this work is to apply data mining methodologies to explore the patterns in data generated by an Ant Colony Algorithm （ACA） performing a searching operation and to develop a rule set searcher which approximates the ACA＇s searcher. An attribute - oriented induction methodology was used to explore the relationship between an operations＇ sequence and its attributes and a set of rules has been developed. At the end of this paper,the experimental results have shown that the proposed approach has good performance with respect to the quality of solution and the speed of computation.     

基于完全图的多支持度阈值关联规则挖掘算法

提出了基于完全图的最大约束条件下多支持度闻值关联规则挖掘改进算法。该算法利用完全图与频繁项集之间的关系，以完全图结点的度作为判断标准，使得寻找单个候选项集Ck（k≥3）的复杂度降低了。在由候选集得到频繁集的过程中，该算法避免了对事务数据库的扫描，从而减小了系统的I/O开销。因此，该算法减小了存储空间，加快了挖掘速度，提高了算法的效率。     

一种具有时序特征的告警关联规则挖掘算法

电信网络中各种频繁告警事件的发生通常具有一定的时序关系,据此提出了一种具有时序特征的告警关联挖掘算法。该方法解决了以往算法中无法发现告警序列之间时序关系的问题。其核心思想是在建立FP-tree时加入告警的时间特征,然后对FP-tree进行挖掘。实验证明,该算法能够快速、准确地从海量数据中挖掘出具有时序特征地关联规则。     

A new classification algorithm based on RGH-tree search

In this paper, we put forward a new classification algorithm based on RGH-Tree search and perform the classification analysis and comparison study. This algorithm can save computing resource and increase the classification efficiency. The experiment shows that this algorithm can get better effect in dealing with three dimensional multi-kind data. We find that the algorithm has better generalization ability for small training set and big testing result.     

利用NetFlow技术构建网络异常监测及分析系统

随着互联网的不断普及,网络已经跟人们的日常生活密不可分,使用者在享受网络所带来的便利的同时,网络上的许多问题也陆续衍生而出,运营商网络正面临着日益严重的安全威胁.作为网络管理者,如何迅速有效地监测网络异常情况,同时准确地掌握整个网络的业务流量模型,供市场部门作决策分析,已成为一项重要的课题.介绍了利用NetFlow技术,构建网络异常监测及分析系统,利用本系统可随时监测网络运行情况,以达到网络稳定顺畅运作及节省经费、人力的目的.     

Detecting modes of transport from unlabelled positioning sensor data

Global positioning systems (GPS) logs recorded in personal devices contain rich information such as travel patterns, locations of frequent visits and place–event associations. There have been rather successful attempts in detecting the mode of transport from GPS logs such as walking, driving or taking a bus, which has found varied applications. However, the best-known schemes either require tedious manual labelling or pre-training process (or both). We present MoDetect (MD), a unsupervised scheme which eliminates the need of manual labelling and pre-training while attaining equal or greater accuracy compared with the best-known supervised methods. MD can also cater for differences in individual's behaviours, and hence may be more widely applicable than the existing schemes. To achieve this, MD relies on Kolmogorov–Smirnov test which offers a theoretical assurance when computing similarity between segments of records. Our analysis shows that the higher speed modes can be better differentiated through a weighted bootstrapping procedure. We also augment the decisions with reference to the transfer probabilities between different modes at locations identified from the GPS records.