Multilevel Association Rules Research Articles

A Novel Approach for Discovery Quantitative Fuzzy Multi-Level Association Rules Mining Using Genetic Algorithm

Quantitative multilevel association rules mining is a central field to realize motivating associations among data components with multiple levels abstractions. The problem of expanding procedures to handle quantitative data has been attracting the attention of many researchers. The algorithms regularly discretize the attribute fields into sharp intervals, and then implement uncomplicated algorithms established for Boolean attributes. Fuzzy association rules mining approaches are intended to defeat such shortcomings based on the fuzzy set theory. Furthermore, most of the current algorithms in the direction of this topic are based on very tiring search methods to govern the ideal support and confidence thresholds that agonize from risky computational cost in searching association rules. To accelerate quantitative multilevel association rules searching and escape the extreme computation, in this paper, we propose a new genetic-based method with significant innovation to determine threshold values for frequent item sets. In this approach, a sophisticated coding method is settled, and the qualified confidence is employed as the fitness function. With the genetic algorithm, a comprehensive search can be achieved and system automation is applied, because our model does not need the user-specified threshold of minimum support. Experiment results indicate that the recommended algorithm can powerfully generate non-redundant fuzzy multilevel association rules.

Effective and Innovative Approaches for Comparing Different Multilevel Association Rule Mining for Feature Extraction: A Review

Today multilevel association rule mining is an emerging field in data mining. Its main goal is to find hidden information in or between levels of abstraction. It is mainly used for decision making for large data. It focuses on the customer relationship management. Apriori algorithm is mainly used for the multilevel association rule mining. Producing large number of candidate item sets and multiple scanning databases is main shortage of the Apriori algorithm. Because of the multilevel association execution time is reduced and throughput increase in new methods.MRA algorithm using Bayesian probability, concept hierarchy ,COFI-tree method, dynamic concept hierarchy are used for increase performance of multilevel association rule mining.

GA‐based multi-level association rule mining approach for defect analysis in the construction industry

Abstract In construction industry, work defects yield time and cost overruns of construction projects and also cause disputes between project participants during construction and operation phases. To date, there hasn't yet been an adequate analytical model to extract useful information from the database of construction defects. The information represented in the form of association rules could enhance quality management via defect prediction and causation analysis. This paper proposes a Genetic Algorithm (GA)-based approach that incorporates the concept hierarchy of construction defects to discover multi-level patterns of defects from the database of defects in the Chinese construction industry during 2000 to 2010. First, the domain knowledge of construction defect is incorporated into a concept hierarchy to adjust mining items at different levels according to the data sparseness and the interestingness of a rule. Second, a GA-based approach is proposed to generate interesting association rules without specified threshold of minimum confidence, taking advantage of the searching capability of GA. Finally, the redundant rules in the mining results are pruned by post-processing method. A test case is selected to demonstrate the feasibility and applicability of the proposed approach within the problem domain. It is concluded that the proposed method provided an effective tool to discover useful knowledge hidden in historical defect cases. The discovered knowledge indicating relationships between defects and defect causes enables project managers to make strategies for estimating and reducing defects.

Comparison of New Multilevel Association Rule Algorithm with MAFIA

Multilevel association rules provide the more precise and specific information. Apriori algorithm is an established algorithm for finding association rules. Fast Apriori implementation is modified to develop new algorithm for finding frequent item sets and mining multilevel association rules. MAFIA is another established algorithm for finding frequent item sets. In this paper, the performance of this new algorithm is analyzed and compared with MAFIA algorithm.

Generating 3rd Level Association Rules Using Fast Apriori Implementation

The mining of association rules and frequent item sets are the main area of interest in recent research activities. The multiple level association rules provide the more meaningful information in comparison to single level association rules which describes only single level concept hierarchy. The Apriori algorithm is most established algorithm for mining the single level association rules. In this study, the fast implementation of Apriori algorithm has been used to generate 3rd level association rules with some modifications in the algorithm. The data coding and data cleaning techniques are used to find the multilevel association rules as they are prerequisite to implement the modified algorithm.

A Genetic Algorithm Based Multilevel Association Rules Mining for Big Datasets

Multilevel association rules mining is an important domain to discover interesting relations between data elements with multiple levels abstractions. Most of the existing algorithms toward this issue are based on exhausting search methods such as Apriori, and FP-growth. However, when they are applied in the big data applications, those methods will suffer for extreme computational cost in searching association rules. To expedite multilevel association rules searching and avoid the excessive computation, in this paper, we proposed a novel genetic-based method with three key innovations. First, we use the category tree to describe the multilevel application data sets as the domain knowledge. Then, we put forward a special tree encoding schema based on the category tree to build the heuristic multilevel association mining algorithm. As the last part of our design, we proposed the genetic algorithm based on the tree encoding schema that will greatly reduce the association rule search space. The method is especially useful in mining multilevel association rules in big data related applications. We test the proposed method with some big datasets, and the experimental results demonstrate the effectiveness and efficiency of the proposed method in processing big data. Moreover, our results also manifest that the algorithm is fast convergent with a limited termination threshold.

A Survey on Retrieval Strategy for Case-Based Reasoning using Modified - USIMSCAR for Object Oriented Cases

Case based reasoning is the problem solving technique which is fully based on the solutions of similar past problems. In CBR system, retrieval is an important part since the overall performance is based on it. Aim of this paper is to retrieve the combined set of each cases and rules relevant to the target drawback, using the combined knowledge of similarity and association. This paper proposes the retrieval strategy in CBR System for object oriented cases using modified - USIMSCAR algorithm. It also argues the performance of the generated association rules using SCAR algorithm in CBR systems. Association rules are created using ARM techniques and OO-datasets are used for evaluation. Modified - USIMSCAR algorithm is applied in medical domain. Keywords: CBR, Association knowledge (AK), Association rule mining (ARM), Case-Based Reasoning, Multilevel association rules, Modified-USIMSCAR(unified similarity and soft- matching class association rule)

Multilevel Association Rule Mining for Bridge Resource Management Based on Immune Genetic Algorithm

This paper is concerned with the problem of multilevel association rule mining for bridge resource management (BRM) which is announced by IMO in 2010. The goal of this paper is to mine the association rules among the items of BRM and the vessel accidents. However, due to the indirect data that can be collected, which seems useless for the analysis of the relationship between items of BIM and the accidents, the cross level association rules need to be studied, which builds the relation between the indirect data and items of BRM. In this paper, firstly, a cross level coding scheme for mining the multilevel association rules is proposed. Secondly, we execute the immune genetic algorithm with the coding scheme for analyzing BRM. Thirdly, based on the basic maritime investigation reports, some important association rules of the items of BRM are mined and studied. Finally, according to the results of the analysis, we provide the suggestions for the work of seafarer training, assessment, and management.

A Database Sanitizing Algorithm for Hiding Sensitive Multi-Level Association Rule Mining

A Database Sanitizing Algorithm for Hiding Sensitive Multi-Level Association Rule Mining

An efficient approach for mining cross-level closed itemsets and minimal association rules using closed itemset lattices

Multilevel knowledge in transactional databases plays a significant role in our real-life market basket analysis. Many researchers have mined the hierarchical association rules and thus proposed various approaches. However, some of the existing approaches produce many multilevel and cross-level association rules that fail to convey quality information. From these large number of redundant association rules, it is extremely difficult to extract any meaningful information. There also exist some approaches that mine minimal association rules, but these have many shortcomings due to their naïve-based approaches. In this paper, we have focused on the need for generating hierarchical minimal rules that provide maximal information. An algorithm has been proposed to derive minimal multilevel association rules and cross-level association rules. Our work has made significant contributions in mining the minimal cross-level association rules, which express the mixed relationship between the generalized and specialized view of the transaction itemsets. We are the first to design an efficient algorithm using a closed itemset lattice-based approach, which can mine the most relevant minimal cross-level association rules. The parent–child relationship of the lattices has been exploited while mining cross-level closed itemset lattices. We have extensively evaluated our proposed algorithm’s efficiency using a variety of real-life datasets and performing a large number of experiments. The proposed algorithm has outperformed the existing related work significantly during the pervasive performance comparison.

Performance Analysis of Modified Algorithm for Finding multilevel Association Rules

Multilevel association rules explore the concept hierarchy at multiple levels which provides more specific information. Apriori algorithm explores the single level association rules. Many implementations are available of Apriori algorithm. Fast Apriori implementation is modified to develop new algorithm for finding multilevel association rules. In this study the performance of this new algorithm is analyzed in terms of running time in seconds.

Interestingness measures and strategies for mining multi-ontology multi-level association rules from gene ontology annotations for the discovery of new GO relationships

The Gene Ontology (GO), a set of three sub-ontologies, is one of the most popular bio-ontologies used for describing gene product characteristics. GO annotation data containing terms from multiple sub-ontologies and at different levels in the ontologies is an important source of implicit relationships between terms from the three sub-ontologies. Data mining techniques such as association rule mining that are tailored to mine from multiple ontologies at multiple levels of abstraction are required for effective knowledge discovery from GO annotation data. We present a data mining approach, Multi-ontology data mining at All Levels (MOAL) that uses the structure and relationships of the GO to mine multi-ontology multi-level association rules. We introduce two interestingness measures: Multi-ontology Support (MOSupport) and Multi-ontology Confidence (MOConfidence) customized to evaluate multi-ontology multi-level association rules. We also describe a variety of post-processing strategies for pruning uninteresting rules. We use publicly available GO annotation data to demonstrate our methods with respect to two applications (1) the discovery of co-annotation suggestions and (2) the discovery of new cross-ontology relationships.

Multi-level association rules and directed graphs for spatial data analysis

We propose a methodology that upgrades the methods of the Lagrangian analysis of surface sea-water parcels. This methodology includes data mining with efficient visualization techniques, namely, spatial–temporal association rules and multi-level directed graphs with different levels of space and time granularity. In the resulting multi-level directed graphs we can intertwine knowledge from various disciplines related to oceanography (in our application) and perform the mining of such graphs. We evaluate the proposed methodology on Lagrangian tracking of virtual particles in the velocity field of the numerical model called the Mediterranean Ocean Forecasting Model (MFS). We describe an efficient algorithm based on label propagation clustering, which finds cycles and paths in multi-level directed graphs and reveals how the number and size of the cycles depend on the seasons. In addition, we offer three interesting results of the visualization and mining of such graphs, that is, the 12months periodicity of the exchange of water masses among sea areas, the separation of Mediterranean Sea circulation in summer and winter situations, obtained with the hierarchical clustering of multi-level directed graphs, and finally, with visualization with multi-level directed graphs we confirm the reversal of sea circulation in the Ionian Sea over the last decades. The aforementioned results received a very favorable evaluation from oceanographic experts.

Spatially enabled emergency event analysis using a multi-level association rule mining method

Spatial attributes are important factors affecting the entire process of emergency management. Despite the importance of spatial attributes in emergency rescue, this subject has not been subjected to thorough examination in previous literature. This paper presents a new method that incorporates spatial predicates describing the spatial relationships between emergency locations and surrounding objects into emergency event analysis. More specifically, we developed three mechanisms to achieve spatially enabled emergency analysis. First, a novel fact constellation schemas that integrate the spatial predicates and non-spatial data of emergency events is created for multi-level analysis. Second, a filtering algorithm is designed to remove redundant predicates from the huge amount of spatial predicates for data association rule mining. Third, by incorporating the filtering method, a multi-level spatial data association algorithm was proposed to realize the analytical function for emergency events. Experiments were conducted, and the results validate the effectiveness of our method.

Different Types of Association Rules Mining Review

In recent years, many application systems have generate large quantities of data, so it is no longer practical to rely on traditional database technique to analyze these data. Data mining offers tools for extracting knowledge from data, leading to significant improvement in the decision-making process. Association rules mining is one of the most important data mining technology. The paper first presents the basic concept of association rule mining, then discuss a few different types of association rules mining including multi-level association rules, multidimensional association rules, weighted association rules, multi-relational association rules, fuzzy association rules.

Cross-Ontology Multi-level Association Rule Mining in the Gene Ontology

The Gene Ontology (GO) has become the internationally accepted standard for representing function, process, and location aspects of gene products. The wealth of GO annotation data provides a valuable source of implicit knowledge of relationships among these aspects. We describe a new method for association rule mining to discover implicit co-occurrence relationships across the GO sub-ontologies at multiple levels of abstraction. Prior work on association rule mining in the GO has concentrated on mining knowledge at a single level of abstraction and/or between terms from the same sub-ontology. We have developed a bottom-up generalization procedure called Cross-Ontology Data Mining-Level by Level (COLL) that takes into account the structure and semantics of the GO, generates generalized transactions from annotation data and mines interesting multi-level cross-ontology association rules. We applied our method on publicly available chicken and mouse GO annotation datasets and mined 5368 and 3959 multi-level cross ontology rules from the two datasets respectively. We show that our approach discovers more and higher quality association rules from the GO as evaluated by biologists in comparison to previously published methods. Biologically interesting rules discovered by our method reveal unknown and surprising knowledge about co-occurring GO terms.

Improvement of Mining Fuzzy Multiple-Level Association Rules from Quantitative Data

Data-mining techniques have been developed to turn data into useful task-oriented knowledge. Most algorithms for mining association rules identify relationships among transactions using binary values and find rules at a single-concept level. Extracting multilevel association rules in transaction databases is most commonly used in data mining. This paper proposes a multilevel fuzzy association rule mining model for extraction of implicit knowledge which stored as quantitative values in transactions. For this reason it uses different support value at each level as well as different membership function for each item. By integrating fuzzy-set concepts, data-mining technologies and multiple-level taxonomy, our method finds fuzzy association rules from transaction data sets. This approach adopts a top-down progressively deepening approach to derive large itemsets and also incorporates fuzzy boundaries instead of sharp boundary intervals. Comparing our method with previous ones in simulation shows that the proposed method maintains higher precision, the mined rules are closer to reality, and it gives ability to mine association rules at different levels based on the user’s tendency as well.

Implementation of Hierarchical Database Attributes Covering in Multidetector A-R Mining Algorithm

With the development of artificial intelligence and data warehouse application development,Intelligent and efficient data mining technology has become the huge data bottleneck, This paper studies stratification theory improved technology to realize the property overrides hierarchical database, Data pretreatment based on the mining algorithm, Through in-depth analysis and research, Improved the A-R algorithm, Realize the problem scope expanded and improved the classical association rules mining algorithm efficiency, Based on the realization of the multilevel association rules mining based on the attribute weights of attributes covering hierarchical database mining methods,To improve the mining knowledge representation systems automation capabilities and data mining algorithm and its application to extended practical problems

Multi-Level Association Rules Mining Algorithm Based on Semantic Relativity

Traditional multi-level association rules mining approaches are based only on database contents. The relations of items in itemset are considered rarely. It leads to generate a lot of meaningless itemsets. Aiming at the problem,multi-level association rules mining algorithm based on semantic relativity is proposed. Domain knowledge is described by Ontology. Every item is seen as a concept in Ontology. Semantic relativity is used to measure the semantic meaning of itemsets. Minimum support of itemset is set according to its length and semantic relativity. Semantic related minimum support with length-decrease is defined to filter meaningless itemsets. Experiments results showed that the method in the paper can improve the efficiency of multi-level association rules mining and generated meaningful rules.

Methods for Mining Cross Level Association Rule In Taxonomy Data Structures

Mining of association rules mainly focuses at a single conceptual level. In a large database of transaction, where each transaction consists of a set of items, and taxonomy on items, it is required to find out the associations at multiple conceptual levels. In this paper, multilevel association rule mining algorithms have been evaluated and compared. And we will discover additional strong association rules in taxonomy data items. The performance indices used for performance comparisons are minimum support threshold at different levels and varying number of transactions.