Maintenance of Association Rules Using Pre-Large Itemsets

2011 ◽  
pp. 44-60 ◽  
Author(s):  
Tzung-Pei Hong ◽  
Ching-Yao Wang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Real World ◽  
Incremental Mining ◽  
Maintenance Costs ◽  
The Past ◽  
Mining Algorithm ◽  
Real World Applications ◽  
Novel Algorithm

Developing an efficient mining algorithm that can incrementally maintain discovered information as a database grows is quite important in the field of data mining. In the past, we proposed an incremental mining algorithm for maintenance of association rules as new transactions were inserted. Deletion of records in databases is, however, commonly seen in real-world applications. In this chapter, we first review the maintenance of association rules from data insertion and then attempt to extend it to solve the data deletion issue. The concept of pre-large itemsets is used to reduce the need for rescanning the original database and to save maintenance costs. A novel algorithm is proposed to maintain discovered association rules for deletion of records. The proposed algorithm doesn’t need to rescan the original database until a number of records have been deleted. If the database is large, then the number of deleted records allowed will be large too. Therefore, as the database grows, our proposed approach becomes increasingly efficient. This characteristic is especially useful for real-world applications.

A FUZZY DATA MINING ALGORITHM FOR INCREMENTAL MINING OF QUANTITATIVE SEQUENTIAL PATTERNS

2005 ◽  
Vol 13 (06) ◽  
pp. 633-652 ◽  
Author(s):  
R. B. V. SUBRAMANYAM ◽  
A. GOSWAMI
Keyword(s):  
Data Mining ◽  
Real World ◽  
Uncertain Data ◽  
Sequential Patterns ◽  
Data Mining Algorithm ◽  
Fuzzy Data ◽  
Incremental Mining ◽  
Data Mining Algorithms ◽  
Mining Algorithm ◽  
Real World Applications

In real world applications, the databases are constantly added with a large number of transactions and hence maintaining latest sequential patterns valid on the updated database is crucial. Existing data mining algorithms can incrementally mine the sequential patterns from databases with binary values. Temporal transactions with quantitative values are commonly seen in real world applications. In addition, several methods have been proposed for representing uncertain data in a database. In this paper, a fuzzy data mining algorithm for incremental mining of sequential patterns from quantitative databases is proposed. Proposed algorithm called IQSP algorithm uses the fuzzy grid notion to generate fuzzy sequential patterns validated on the updated database containing the transactions in the original database and in the incremental database. It uses the information about sequential patterns that are already mined from original database and avoids start-from-scratch process. Also, it minimizes the number of candidates to check as well as number of scans to original database by identifying the potential sequences in incremental database.

Ordering policy estimation for high utility item-sets considering negative item values in large databases

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Data Mining ◽  
Real World ◽  
Defective Items ◽  
Utility Mining ◽  
Mining Algorithm ◽  
Negative Item ◽  
Ordering Policy ◽  
Large Databases ◽  
Real World Applications ◽  
High Utility

Utility mining with negative item values has recently received interest in the data mining field due to its practical considerations. Previously, the values of utility item-sets have been taken into consideration as positive. However, in real-world applications an item-set may be related to negative item values. This paper presents a method for redesigning the ordering policy by including high utility item-sets with negative items. Initially, utility mining algorithm is used to find high utility item-sets. Then, ordering policy is estimated for high utility items considering defective and non-defective items. A numerical example is illustrated to validate the results

Against generalisation: Data-driven decisions need context to be human-compatible

2021 ◽  
pp. 026638212110619
Author(s):  
Sharon Richardson
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Theoretical Frameworks ◽  
The Past ◽  
Real World Applications ◽  
Contextual Framework ◽  
Data Driven Decisions

During the past two decades, there have been a number of breakthroughs in the fields of data science and artificial intelligence, made possible by advanced machine learning algorithms trained through access to massive volumes of data. However, their adoption and use in real-world applications remains a challenge. This paper posits that a key limitation in making AI applicable has been a failure to modernise the theoretical frameworks needed to evaluate and adopt outcomes. Such a need was anticipated with the arrival of the digital computer in the 1950s but has remained unrealised. This paper reviews how the field of data science emerged and led to rapid breakthroughs in algorithms underpinning research into artificial intelligence. It then discusses the contextual framework now needed to advance the use of AI in real-world decisions that impact human lives and livelihoods.

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

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yu-Lung Hsieh ◽  
Don-Lin Yang ◽  
Jungpin Wu
Keyword(s):  
Financial Markets ◽  
Real World ◽  
Traditional Approach ◽  
Rule Mining ◽  
Mining Algorithm ◽  
Large Databases ◽  
Real World Applications ◽  
Trading Model ◽  
Mining Algorithms ◽  
Infant Stage

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.

scholarly journals INDUSTRIAL APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS

2014 ◽  
pp. 8-20
Author(s):  
Kurosh Madani
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real World ◽  
Industrial Applications ◽  
Large Field ◽  
Nonlinear Functions ◽  
The Past ◽  
Real World Applications ◽  
Ann Models ◽  
Artificial Neural

In a large number of real world dilemmas and related applications the modeling of complex behavior is the central point. Over the past decades, new approaches based on Artificial Neural Networks (ANN) have been proposed to solve problems related to optimization, modeling, decision making, classification, data mining or nonlinear functions (behavior) approximation. Inspired from biological nervous systems and brain structure, Artificial Neural Networks could be seen as information processing systems, which allow elaboration of many original techniques covering a large field of applications. Among their most appealing properties, one can quote their learning and generalization capabilities. The main goal of this paper is to present, through some of main ANN models and based techniques, their real application capability in real world industrial dilemmas. Several examples through industrial and real world applications have been presented and discussed.

scholarly journals Sliding-Window Thompson Sampling for Non-Stationary Settings

2020 ◽  
Vol 68 ◽  
pp. 311-364
Author(s):  
Francesco Trovo ◽  
Stefano Paladino ◽  
Marcello Restelli ◽  
Nicola Gatti
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Sliding Window ◽  
Upper Bounds ◽  
Decision Problems ◽  
Sequential Decision ◽  
Thompson Sampling ◽  
The Past ◽  
Real World Applications ◽  
Window Approach

Multi-Armed Bandit (MAB) techniques have been successfully applied to many classes of sequential decision problems in the past decades. However, non-stationary settings -- very common in real-world applications -- received little attention so far, and theoretical guarantees on the regret are known only for some frequentist algorithms. In this paper, we propose an algorithm, namely Sliding-Window Thompson Sampling (SW-TS), for nonstationary stochastic MAB settings. Our algorithm is based on Thompson Sampling and exploits a sliding-window approach to tackle, in a unified fashion, two different forms of non-stationarity studied separately so far: abruptly changing and smoothly changing. In the former, the reward distributions are constant during sequences of rounds, and their change may be arbitrary and happen at unknown rounds, while, in the latter, the reward distributions smoothly evolve over rounds according to unknown dynamics. Under mild assumptions, we provide regret upper bounds on the dynamic pseudo-regret of SW-TS for the abruptly changing environment, for the smoothly changing one, and for the setting in which both the non-stationarity forms are present. Furthermore, we empirically show that SW-TS dramatically outperforms state-of-the-art algorithms even when the forms of non-stationarity are taken separately, as previously studied in the literature.

An Efficient Pruning and Filtering Strategy to Mine Partial Periodic Patterns from a Sequence of Event Sets

2014 ◽  
Vol 10 (2) ◽  
pp. 18-38 ◽  
Author(s):  
Kung-Jiuan Yang ◽  
Tzung-Pei Hong ◽  
Yuh-Min Chen ◽  
Guo-Cheng Lan
Keyword(s):  
Time Series ◽  
Real World ◽  
Oil Price ◽  
Time Stamp ◽  
Single Event ◽  
Periodic Patterns ◽  
Event Time ◽  
The Past ◽  
Real World Applications ◽  
Speed Up

Partial periodic patterns are commonly seen in real-world applications. The major problem of mining partial periodic patterns is the efficiency problem due to a huge set of partial periodic candidates. Although some efficient algorithms have been developed to tackle the problem, the performance of the algorithms significantly drops when the mining parameters are set low. In the past, the authors have adopted the projection-based approach to discover the partial periodic patterns from single-event time series. In this paper, the authors extend it to mine partial periodic patterns from a sequence of event sets which multiple events concurrently occur at the same time stamp. Besides, an efficient pruning and filtering strategy is also proposed to speed up the mining process. Finally, the experimental results on a synthetic dataset and real oil price dataset show the good performance of the proposed approach.

PROVIDING TIMELY UPDATED SEQUENTIAL PATTERNS IN DECISION MAKING

2010 ◽  
Vol 09 (06) ◽  
pp. 873-888 ◽  
Author(s):  
TZUNG-PEI HONG ◽  
CHING-YAO WANG ◽  
CHUN-WEI LIN
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Real World ◽  
Experimental Results ◽  
Sequential Patterns ◽  
The Past ◽  
Large Databases ◽  
Real World Applications ◽  
Critical Task ◽  
Maintenance Process

Mining knowledge from large databases has become a critical task for organizations. Managers commonly use the obtained sequential patterns to make decisions. In the past, databases were usually assumed to be static. In real-world applications, however, transactions may be updated. In this paper, a maintenance algorithm for rapidly updating sequential patterns for real-time decision making is proposed. The proposed algorithm utilizes previously discovered large sequences in the maintenance process, thus greatly reducing the number of database rescans and improving performance. Experimental results verify the performance of the proposed approach. The proposed algorithm provides real-time knowledge that can be used for decision making.

From Single Biometrics to Multi-Biometrics

2011 ◽  
pp. 254-272
Author(s):  
David Zhang ◽  
Fengxi Song ◽  
Yong Xu ◽  
Zhizhen Liang
Keyword(s):  
Performance Evaluation ◽  
Research And Development ◽  
Real World ◽  
System Performance ◽  
Biometric System ◽  
The Past ◽  
Biometric Systems ◽  
Real World Applications ◽  
Biometric Trait

In the past decades while biometrics attracts increasing attention of researchers, people also have found that the biometric system using a single biometric trait may not satisfy the demand of some real-world applications. Diversity of biometric traits also means that they may have different performance such as accuracy and reliability. Multi-biometric applications emerging in recent years are a big progress of biometrics. They can overcome some shortcomings of the single biometric system and can perform well in improving the system performance. In this chapter we describe a number of definitions on biometrics, categories and fusion strategies of multi-biometrics as well as the performance evaluation on the biometric system. The first section of this chapter describes some concepts, motivation and justification of multi-biometrics. Section 12.2 provides some definitions and notations of biometric and multi-biometric technologies. Section 12.3 is mainly related to performance evaluation of various types of biometric systems. Section 12.4 briefly presents research and development of multi-biometrics.

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