Emergent intertransaction association rules for abnormality detection in intelligent environments

This paper is concerned with identifying anomalous behaviour of people in smart environments. We propose the use of emergent transaction mining and the use of the extended frequent pattern tree as a basis. Our experiments on two data sets demonstrate that emergent intertransaction associations are able to detect abnormality present in real world data and that both short and long term behavioural changes can be discovered. The use of intertransaction associations is shown to be advantageous in the detection of temporal associationanomalies otherwise not readily detectable by traditional "market basket" intratransaction mining

Stock market prediction using weighted inter-transaction class association rule mining and evolutionary algorithm

Evolutionary computation and data mining are two fascinating fields that have attracted many researchers. This paper proposes a new rule mining method, named genetic network programming (GNP), to solve the prediction problem using the evolutionary algorithm. Compared with the conventional association rule methods that do not consider the weight factor, the proposed algorithm provides many advantages in financial prediction, since it can discover relationships among the attributes of different transactions. Experimental results on data from the New York Exchange Market show that the new method outperforms other conventional models in terms of both accuracy and profitability, and the proposed method can establish more important and accurate rules than the conventional methods. The results confirmed the effectiveness of the proposed data mining method in financial prediction

Temporal Text Association Rule and Its Applications in the Statistical Analysis of Network Public Opinion

自古以来，社会舆情便是维系社会经济发展与进步的重要一环。随着社会科技的进步与互联网的广泛应用，网络工具正逐步成为民众舆情传播与获取的关键手段之一，网络舆情已经成为社会民众舆情的主要组成部分。作为网络舆情内容的主要构成部分，半结构化的文本数据一直是传统舆情分析中主要的分析对象之一。与此同时，网络舆情信息的实时性特征也凸显了时间属性在其数据结构中的重要地位。随着统计学与数据挖掘研究的不断进展，文本挖掘与时态数据挖掘技术越来越受到学界的关注，将二者结合运用于现代网络舆情的系统性分析框架中，既是网络舆情分析发展的中的重要一环，也是必经之路。 在对文本挖掘及关联规则重要概念整理与统计学阐述的基础上，本...As we known, social public opinion is an important link to maintain social and economic development and progress. With the wide application of the progress of social technology and the Internet, network tool is gradually becoming one of the key ways that public opinion spread around or be got through, which means that network public opinion has become a main part of social public opinion. As the m...学位：经济学硕士院系专业：经济学院_统计学学号：1542013115202

DISCOVERING PATTERNS FROM TEMPORAL DATABASES USING TEMPORAL ASSOCIATION RULE

Data mining is the process of discovering and examining data from diverse viewpoint, using automatic or semiautomatic techniques to remove knowledge or useful information and discover correlations or meaningful patterns and rules from large databases. One of the most vital characteristic missed by the traditional data mining systems is their capability to record and process time-varying aspects of the real world databases. . Temporal data mining, which mines or discovers knowledge and patterns from temporal databases, is an extension of data mining with capability to include time attribute analysis. The pattern discovery task of temporal data mining discovers all patterns of interest from a large dataset. This paper presents an overview of temporal data mining and focus on pattern discovery using temporal association rules

An Optimal Approach for Mining Rare Causal Associations to Detect ADR Signal Pairs

Abstract- Adverse Drug Reaction (ADR) is one of the most important issues in the assessment of drug safety. In fact, many adverse drug reactions are not discovered during limited premarketing clinical trials; instead, they are only observed after long term post-marketing surveillance of drug usage. In light of this, the detection of adverse drug reactions, as early as possible, is an important topic of research for the pharmaceutical industry. Recently, large numbers of adverse events and the development of data mining technology have motivated the development of statistical and data mining methods for the detection of ADRs. These stand-alone methods, with no integration into knowledge discovery systems, are tedious and inconvenient for users and the processes for exploration are time-consuming. This paper proposes an interactive system platform for the detection of ADRs. By integrating an ADR data warehouse and innovative data mining techniques, the proposed system not only supports OLAP style multidimensional analysis of ADRs, but also allows the interactive discovery of associations between drugs and symptoms, called a drug-ADR association rule, which can be further, developed using other factors of interest to the user, such as demographic information. The experiments indicate that interesting and valuable drug-ADR association rules can be efficiently mined. Index Terms- In this paper, we try to employ a knowledgebased approach to capture the degree of causality of an event pair within each sequence and we are going to match the data which was previously referred or suggested for treatment. � It is majorly used for Immediate Treatment for patients. However, mining the relationships between Drug and its Signal Reaction will be treated by In-Experienced Physician’

Effective Application of Improved Profit-Mining Algorithm for the Interday Trading Model

Many real world applications of association rule mining from large databases help users make better decisions. However, they do not work well in financial markets at this time. In addition to a high profit, an investor also looks for a low risk trading with a better rate of winning. The traditional approach of using minimum confidence and support thresholds needs to be changed. Based on an interday model of trading, we proposed effective profit-mining algorithms which provide investors with profit rules including information about profit, risk, and winning rate. Since profit-mining in the financial market is still in its infant stage, it is important to detail the inner working of mining algorithms and illustrate the best way to apply them. In this paper we go into details of our improved profit-mining algorithm and showcase effective applications with experiments using real world trading data. The results show that our approach is practical and effective with good performance for various datasets

Logical Linked Data Compression

Linked data has experienced accelerated growth in recent years. With the continuing proliferation of structured data, demand for RDF compression is becoming increasingly important. In this study, we introduce a novel lossless compression technique for RDF datasets, called Rule Based Compression (RB Compression) that compresses datasets by generating a set of new logical rules from the dataset and removing triples that can be inferred from these rules. Unlike other compression techniques, our approach not only takes advantage of syntactic verbosity and data redundancy but also utilizes semantic associations present in the RDF graph. Depending on the nature of the dataset, our system is able to prune more than 50% of the original triples without affecting data integrity