Inference by Believers in the Law of Small Numbers

Many people believe in the "Law of Small Numbers," exaggerating the degree to which a small sample resembles the population from which it is drawn. To model this, I assume that a person exaggerates the likelihood that a short sequence of i.i.d. signals resembles the long-run rate at which those signals are generated. Such a person believes in the "gambler's fallacy", thinking early draws of one signal increase the odds of next drawing other signals. When uncertain about the rate, the person over-infers from short sequences of signals, and is prone to think the rate is more extreme than it is. When the person makes inferences about the frequency at which rates are generated by different sources -- such as the distribution of talent among financial analysts -- based on few observations from each source, he tends to exaggerate how much variance there is in the rates. Hence, the model predicts that people may pay for financial advice from "experts" whose expertise is entirely illusory. Other economic applications are discussed.

Data mining query language design and implementation.

Xiaolei Yuan.Thesis submitted in: December 2003.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 95-101).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.1.1 --- Data Mining: A New Wave of Database Applications --- p.1Chapter 1.1.2 --- Association Rule Mining --- p.4Chapter 1.2 --- Motivation --- p.7Chapter 1.3 --- Main Contribution --- p.8Chapter 1.4 --- Thesis Organization --- p.9Chapter 2 --- Literature Review --- p.10Chapter 2.1 --- Data mining and association rule mining --- p.10Chapter 2.2 --- Integration data mining with DBMS --- p.11Chapter 2.3 --- Query language design for association rule mining --- p.12Chapter 2.4 --- Unified data mining models --- p.15Chapter 2.5 --- Other topics --- p.15Chapter 3 --- A New Data Mining Query Language M2MQL --- p.17Chapter 3.1 --- Simple item-based association rule --- p.18Chapter 3.1.1 --- One rule set --- p.19Chapter 3.1.2 --- Rule set and Source data set --- p.22Chapter 3.1.3 --- New rule sets from existing ones --- p.24Chapter 3.2 --- Generalized item-based association rules --- p.25Chapter 3.3 --- CREATE RULE and SELECT RULE Primitive --- p.32Chapter 4 --- The Algebra in M2MQL --- p.33Chapter 4.1 --- Review of nested relations --- p.33Chapter 4.1.1 --- Concepts of nested relation --- p.34Chapter 4.1.2 --- Nested relation and association rule mining --- p.35Chapter 4.2 --- Nested relational algebra --- p.36Chapter 4.3 --- Specific data mining algebra --- p.39Chapter 4.3.1 --- POWERSET p --- p.40Chapter 4.3.2 --- SET-CONTAINMENT-JOIN xc --- p.40Chapter 4.3.3 --- Functional operators --- p.42Chapter 5 --- Mining On Top of M2MQL --- p.50Chapter 5.1 --- Problem statement --- p.50Chapter 5.2 --- Frequency Counting Phase --- p.52Chapter 5.3 --- Frequent Itemset Generation Phase --- p.54Chapter 5.4 --- Rule Generation Phase --- p.57Chapter 5.5 --- Summary --- p.64Chapter 6 --- Conclusions and Future Work --- p.65Chapter 6.1 --- What we have achieved --- p.65Chapter 6.2 --- What is ahead --- p.66Chapter 6.2.1 --- Issues of Query Optimization --- p.66Chapter 6.2.2 --- Issues of Expanding Table Forms --- p.67Chapter A --- General Syntax of M2MQL --- p.68Chapter B --- Syntax and Example for MSQL --- p.71Chapter B.1 --- Syntax of MSQL --- p.71Chapter B.2 --- Example --- p.73Chapter C --- Syntax and Example for MINE RULE --- p.76Chapter C.1 --- syntax of MINE RULE --- p.76Chapter C.2 --- Example --- p.77Chapter C.2.1 --- Counting Groups --- p.78Chapter C.2.2 --- Making Couples of Clusters --- p.79Chapter C.2.3 --- Extracting Bodies --- p.80Chapter C.2.4 --- Extracting Rules --- p.80Bibliography --- p.8

New Approaches in Multi-View Clustering

Many real-world datasets can be naturally described by multiple views. Due to this, multi-view learning has drawn much attention from both academia and industry. Compared to single-view learning, multi-view learning has demonstrated plenty of advantages. Clustering has long been serving as a critical technique in data mining and machine learning. Recently, multi-view clustering has achieved great success in various applications. To provide a comprehensive review of the typical multi-view clustering methods and their corresponding recent developments, this chapter summarizes five kinds of popular clustering methods and their multi-view learning versions, which include k-means, spectral clustering, matrix factorization, tensor decomposition, and deep learning. These clustering methods are the most widely employed algorithms for single-view data, and lots of efforts have been devoted to extending them for multi-view clustering. Besides, many other multi-view clustering methods can be unified into the frameworks of these five methods. To promote further research and development of multi-view clustering, some popular and open datasets are summarized in two categories. Furthermore, several open issues that deserve more exploration are pointed out in the end

A Planning Approach to Migrating Domain-specific Legacy Systems into Service Oriented Architecture

The planning work prior to implementing an SOA migration project is very important for its success. Up to now, most of this kind of work has been manual work. An SOA migration planning approach based on intelligent information processing methods is addressed to semi-automate the manual work. This thesis will investigate the principle research question: “How can we obtain SOA migration planning schemas (semi-) automatically instead of by traditional manual work in order to determine if legacy software systems should be migrated to SOA computation environment?”. The controlled experiment research method has been adopted for directing research throughout the whole thesis. Data mining methods are used to analyse SOA migration source and migration targets. The mined information will be the supplementation of traditional analysis results. Text similarity measurement methods are used to measure the matching relationship between migration sources and migration targets. It implements the quantitative analysis of matching relationships instead of common qualitative analysis. Concretely, an association rule and sequence pattern mining algorithms are proposed to analyse legacy assets and domain logics for establishing a Service model and a Component model. These two algorithms can mine all motifs with any min-support number without assuming any ordering. It is better than the existing algorithms for establishing Service models and Component models in SOA migration situations. Two matching strategies based on keyword level and superficial semantic levels are described, which can calculate the degree of similarity between legacy components and domain services effectively. Two decision-making methods based on similarity matrix and hybrid information are investigated, which are for creating SOA migration planning schemas. Finally a simple evaluation method is depicted. Two case studies on migrating e-learning legacy systems to SOA have been explored. The results show the proposed approach is encouraging and applicable. Therefore, the SOA migration planning schemas can be created semi-automatically instead of by traditional manual work by using data mining and text similarity measurement methods

Bridgewater College Catalog, Session 1990-91

https://digitalcommons.bridgewater.edu/college_catalogs/1101/thumbnail.jp

Bridgewater College Catalog, Session 1994-95

https://digitalcommons.bridgewater.edu/college_catalogs/1105/thumbnail.jp

QMCPACK: Advances in the development, efficiency, and application of auxiliary field and real-space variational and diffusion Quantum Monte Carlo

We review recent advances in the capabilities of the open source ab initio Quantum Monte Carlo (QMC) package QMCPACK and the workflow tool Nexus used for greater efficiency and reproducibility. The auxiliary field QMC (AFQMC) implementation has been greatly expanded to include k-point symmetries, tensor-hypercontraction, and accelerated graphical processing unit (GPU) support. These scaling and memory reductions greatly increase the number of orbitals that can practically be included in AFQMC calculations, increasing accuracy. Advances in real space methods include techniques for accurate computation of band gaps and for systematically improving the nodal surface of ground state wavefunctions. Results of these calculations can be used to validate application of more approximate electronic structure methods including GW and density functional based techniques. To provide an improved foundation for these calculations we utilize a new set of correlation-consistent effective core potentials (pseudopotentials) that are more accurate than previous sets; these can also be applied in quantum-chemical and other many-body applications, not only QMC. These advances increase the efficiency, accuracy, and range of properties that can be studied in both molecules and materials with QMC and QMCPACK