Machine Learning and Association Rules
نویسندگان
چکیده
The tutorial will start by reviewing the similarities and differences between statistics, machine learning and data mining. Then we will take a closer look at the knowledge discovery process as described by the CRISP-DM methodology. Here we will focus on various types of machine learning algorithms used for the modeling step and on the statistical approaches and methods used in these algorithms. Attention will primarily be centered on different types of association rules. We will introduce the basic principles of the GUHA method which combines logical and statistical approaches to association rules mining and we will discuss the observational calculi used, the logic of association rules and their applications. We will also show how these principles have been implemented in the LISp-Miner system and how this system can be used to solve real machine learning and data mining tasks.
منابع مشابه
Explain the theoretical and practical model of automatic facade design intelligence in the process of implementing the rules and regulations of facade design and drawing
Artificial intelligence has been trying for decades to create systems with human capabilities, including human-like learning; Therefore, the purpose of this study is to discover how to use this field in the process of learning facade design, specifically learning the rules and standards and national regulations related to the design of facades of residential buildings by machine with a machine ...
متن کاملA Learning Theory Framework for Association Rules and Sequential Events A Learning Theory Framework for Association Rules and Sequential Events
We present a framework and generalization analysis for the use of association rules in the setting of supervised learning. We are specifically interested in a sequential event prediction problem where data are revealed one by one, and the goal is to determine what will next be revealed. In the context of this problem, algorithms based on association rules have a distinct advantage over classica...
متن کاملQuantMiner for mining quantitative association rules
In this paper, we propose QUANTMINER, a mining quantitative association rules system. This system is based on a genetic algorithm that dynamically discovers “good” intervals in association rules by optimizing both the support and the confidence. The experiments on real and artificial databases have shown the usefulness of QUANTMINER as an interactive, exploratory data mining tool.
متن کاملLearning theory analysis for association rules and sequential event prediction
We present a theoretical analysis for prediction algorithms based on association rules. As part of this analysis, we introduce a problem for which rules are particularly natural, called “sequential event prediction.” In sequential event prediction, events in a sequence are revealed one by one, and the goal is to determine which event will next be revealed. The training set is a collection of pa...
متن کاملA Controller Design with ANFIS Architecture Attendant Learning Ability for SSSC-Based Damping Controller Applied in Single Machine Infinite Bus System
Static Synchronous Series Compensator (SSSC) is a series compensating Flexible AC Transmission System (FACTS) controller for maintaining to the power flow control on a transmission line by injecting a voltage in quadrature with the line current and in series mode with the line. In this work, an Adaptive Network-based Fuzzy Inference System controller (ANFISC) has been proposed for controlling o...
متن کامل