Rule-based Mining Research Articles

Prediction of land use for the next 30 years using the PLUS model's multi-scenario simulation in Guizhou Province, China

Land use changes significantly impact the structure and functioning of ecosystems. The current research focus lies in how to utilize economic and policy instruments to regulate conflicts among stakeholders effectively. The objective is to facilitate rational planning and sustainable development of land utilization resources. The PLUS model integrates a rule-based mining method for land expansion analysis and a CA model based on multi-type stochastic seeding mechanism, which can be used to mine the driving factors of land expansion and predict the patch-level evolution of land use landscapes. Using the PLUS model, a simulation was conducted to study the future land use distribution in the research area over the next 30 years. Based on land use data from Guizhou Province in 2000, 2010, and 2020, a total of 16 driving factors were selected from three aspects: geographical environment, transportation network, and socio-economic conditions. Four scenarios, namely natural development, urban development, ecological conservation, and farmland rotection, were established. Comparative analysis of the simulated differences among the various scenarios was performed. (1) The overall accuracy of the land use simulation using the PLUS model in the study area was 0.983, with a Kappa coefficient of 0.972 and a FoM coefficient of 0.509. The research accuracy meets the simulation requirements. (2) Through the simulation of four different scenarios, the study investigated the land use changes in Guizhou Province over the next 30 years. Each scenario exhibited distinct impacts on land utilization. Comprehensive comparison of the different simulation results revealed that the farmland protection scenario aligns with the sustainable development goals of the research area. Currently, there is a relative scarcity of research on land use simulation, particularly in model application, for Guizhou Province. This study aims to provide a reference for the rational planning of land resources and high-quality urban construction in Guizhou, promoting the high-quality economic development in tandem with advanced ecological and environmental protection.

Understanding Contrail Business Processes through Hierarchical Clustering: A Multi-Stage Framework

Real-world business processes are dynamic, with event logs that are generally unstructured and contain heterogeneous business classes. Process mining techniques derive useful knowledge from such logs but translating them into simplified and logical segments is crucial. Complexity is increased when dealing with business processes with a large number of events with no outcome labels. Techniques such as trace clustering and event clustering, tend to simplify the complex business logs but the resulting clusters are generally not understandable to the business users as the business aspects of the process are not considered while clustering the process log. In this paper, we provided a multi-stage hierarchical framework for business-logic driven clustering of highly variable process logs with extensively large number of events. Firstly, we introduced a term contrail processes for describing the characteristics of such complex real-world business processes and their logs presenting contrail-like models. Secondly, we proposed an algorithm Novel Hierarchical Clustering (NoHiC) to discover business-logic driven clusters from these contrail processes. For clustering, the raw event log is initially decomposed into high-level business classes, and later feature engineering is performed exclusively based on the business-context features, to support the discovery of meaningful business clusters. We used a hybrid approach which combines rule-based mining technique with a novel form of agglomerative hierarchical clustering for the experiments. A case-study of a CRM process of the UK’s renowned telecommunication firm is presented and the quality of the proposed framework is verified through several measures, such as cluster segregation, classification accuracy, and fitness of the log. We compared NoHiC technique with two trace clustering techniques using two real world process logs. The discovered clusters through NoHiC are found to have improved fitness as compared to the other techniques, and they also hold valuable information about the business context of the process log.

Enhanced decision support system to predict and prevent hypertension using computational intelligence techniques

Medical decision support systems have been a core of intense research for years. The ongoing study shows that artificial intelligence has been accustomed to probe risk factors for hypertension. Factors, like health-damaging personal behaviors and changes in lifestyle and environment, are major contributors to chronic diseases. The goal of this research was to forecast the risk of developing hypertension by revealing hidden patterns in medical datasets. Quality of the data is the key to enhance the performance of learning model. But most healthcare data suffer from class imbalance problem, which induce the need for an intelligent model which can learn from such grimy data. This paper incorporates a novel approach by combing learning model and rule-based mining to offer decision support. Typically, the proposed work comprises two main implications. First suggests an intelligent learning model using boosting-based support vector machine to diagnose and expose multi-class categories in the imbalanced datasets. Finally, the enhanced predictive model is built upon the classification solution which will portray the innate data similarities. An intelligent fuzzy-based approach was employed to recognize frequent behavioral patterns. Based on these rules, valid decisions could be made to prevent hypertension. The suggested enhanced model is evaluated using a real-time hypertension dataset obtained through primary health centers. With the combination of ensemble strategies, the proposed intelligent learning model attains high classification accuracy for the imbalanced dataset above the traditional model. Thus, the efficient integration of personalized behavior with health data could provide a better understanding regarding patient health. In future this can serve as an eye toward personalized medicine.

Decision trees

Decision trees trace their origins to the era of the early development of written records. This history illustrates a major strength of trees: exceptionally interpretable results which have an intuitive tree‐like display which, in turn, enhances understanding and the dissemination of results. The computational origins of decision trees—sometimes called classification trees or regression trees—are models of biological and cognitive processes. This common heritage drives complementary developments of both statistical decision trees and trees designed for machine learning. The unfolding and progressive elucidation of the various features of trees throughout their early history in the late 20th century is discussed along with the important associated reference points and responsible authors. Statistical approaches, such as a hypothesis testing and various resampling approaches, have coevolved along with machine learning implementations. This had resulted in exceptionally adaptable decision tree tools, appropriate for various statistical and machine learning tasks, across various levels of measurement, with varying levels of data quality. Trees are robust in the presence of missing data and offer multiple ways of incorporating missing data in the resulting models. Although trees are powerful, they are also flexible and easy to use methods. This assures the production of high quality results that require few assumptions to deploy. The treatment ends with a discussion of the most current developments which continue to rely on the synergies and cross‐fertilization between statistical and machine learning communities. Current developments with the emergence of multiple trees and the various resampling approaches that are employed are discussed. WIREs Comput Stat 2013, 5:448–455. doi: 10.1002/wics.1278This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification Statistical Learning and Exploratory Methods of the Data Sciences > Pattern Recognition Statistical Learning and Exploratory Methods of the Data Sciences > Rule-Based Mining

Algorithm quasi‐optimal (AQ) learning

AbstractThe algorithm quasi‐optimal (AQ) is a powerful machine learning methodology aimed at learning symbolic decision rules from a set of examples and counterexamples. It was first proposed in the late 1960s to solve the Boolean function satisfiability problem and further refined over the following decade to solve the general covering problem. In its newest implementations, it is a powerful but yet little explored methodology for symbolic machine learning classification. It has been applied to solve several problems from different domains, including the generation of individuals within an evolutionary computation framework. The current article introduces the main concepts of the AQ methodology and describes AQ for source detection(AQ4SD), a tailored implementation of the AQ methodology to solve the problem of finding the sources of atmospheric releases using distributed sensor measurements. The AQ4SD program is tested to find the sources of all the releases of the prairie grass field experiment. Copyright © 2010 John Wiley & Sons, Inc.This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Knowledge Discovery Statistical Learning and Exploratory Methods of the Data Sciences > Rule-Based Mining