Quantitative Association Rule Mining Research Articles

A framework for knowledge discovery in massive building automation data and its application in building diagnostics

Building Automation System (BAS) plays an important role in building operation nowadays. A huge amount of building operational data is stored in BAS; however, the data can seldom be effectively utilized due to the lack of powerful tools for analyzing the large data. Data mining (DM) is a promising technology for discovering knowledge hidden in large data. This paper presents a generic framework for knowledge discovery in massive BAS data using DM techniques. The framework is specifically designed considering the low quality and complexity of BAS data, the diversity of advanced DM techniques, as well as the integration of knowledge discovered by DM techniques and domain knowledge in the building field. The framework mainly consists of four phases, i.e., data exploration, data partitioning, knowledge discovery, and post-mining. The framework is applied to analyze the BAS data of the tallest building in Hong Kong. The analysis of variance (ANOVA) method is adopted to identify the most significant time variables to the aggregated power consumption. Then the clustering analysis is used to identify the typical operation patterns in terms of power consumption. Eight operation patterns have been identified and therefore the entire BAS data are partitioned into eight subsets. The quantitative association rule mining (QARM) method is adopted for knowledge discovery in each subset considering most of BAS data are numeric type. To enhance the efficiency of the post-mining phase, two indices are proposed for fast and conveniently identifying and utilizing potentially interesting rules discovered by QARM. The knowledge discovered is successfully used for understanding the building operating behaviors, identifying non-typical operating conditions and detecting faulty conditions.

Attribute Level Clustering Approach to Quantitative Association Rule Mining

Generating rules from quantitative data has been widely studied ever since Agarwal and Srikanth explored the problem through their works on association rule mining. Discretization of the ranges of the attributes has been one of the challenging tasks in quantitative association rule mining that guides the rules generated. Also several algorithms are being proposed for fast identification of frequent item sets from large data sets. In this paper a new data driven partitioning algorithm has been proposed to discretize the ranges of the attributes. Also a new approach has been presented to create meta data for the given data set from which frequent item sets can be generated quickly for any given support counts. General Terms Information systems, Data mining, Information systems, Association rules

A New Multiobjective Evolutionary Algorithm for Mining a Reduced Set of Interesting Positive and Negative Quantitative Association Rules

Most of the algorithms for mining quantitative association rules focus on positive dependencies without paying particular attention to negative dependencies. The latter may be worth taking into account, however, as they relate the presence of certain items to the absence of others. The algorithms used to extract such rules usually consider only one evaluation criterion in measuring the quality of generated rules. Recently, some researchers have framed the process of extracting association rules as a multiobjective problem, allowing us to jointly optimize several measures that can present different degrees of trade-off depending on the dataset used. In this paper, we propose MOPNAR, a new multiobjective evolutionary algorithm, in order to mine a reduced set of positive and negative quantitative association rules with low computational cost. To accomplish this, our proposal extends a recent multiobjective evolutionary algorithm based on decomposition to perform an evolutionary learning of the intervals of the attributes and a condition selection for each rule, while introducing an external population and a restarting process to store all the nondominated rules found and to improve the diversity of the rule set obtained. Moreover, this proposal maximizes three objectives-comprehensibility, interestingness, and performance-in order to obtain rules that are interesting, easy to understand, and provide good coverage of the dataset. The effectiveness of the proposed approach is validated over several real-world datasets.

A Survey of Fuzzy Based Association Rule Mining to Find CoOccurrence Relationships

Data mining is the analysis step of the Knowledge Discovery in Databases process, or KDD. It is the process that results in the discovery of new patterns in large data sets. It utilizes methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract knowledge from an existing data set and transform it into a human-understandable structure.In data mining, association rule learning is a popular and well researched method for discovering interesting relations between variables in large databases. Association rules are usually required to satisfy a user-specified minimum support and a user-specified minimum confidence at the same time. A fuzzy association rule mining (firstly expressed as quantitative association rule mining) has been proposed using fuzzy sets such that quantitative and categorical attributes can be handled. A fuzzy rule represents each item as pair. Fuzzy logic softens the effect of sharp boundary intervals and solves the problem of uncertainty present in data relationships.In this paper we represent a survey of Association Rule Mining Using Fuzzy Algorithm. The techniques are categorized based upon different approaches. This paper provides the major advancement in the approaches for association rule mining using fuzzy algorithms.

Discovering gene association networks by multi-objective evolutionary quantitative association rules

In the last decade, the interest in microarray technology has exponentially increased due to its ability to monitor the expression of thousands of genes simultaneously. The reconstruction of gene association networks from gene expression profiles is a relevant task and several statistical techniques have been proposed to build them. The problem lies in the process to discover which genes are more relevant and to identify the direct regulatory relationships among them. We developed a multi-objective evolutionary algorithm for mining quantitative association rules to deal with this problem. We applied our methodology named GarNet to a well-known microarray data of yeast cell cycle. The performance analysis of GarNet was organized in three steps similarly to the study performed by Gallo et al. GarNet outperformed the benchmark methods in most cases in terms of quality metrics of the networks, such as accuracy and precision, which were measured using YeastNet database as true network. Furthermore, the results were consistent with previous biological knowledge.

Enhanced New User Recommendations based on Quantitative Association Rule Mining

In the era of information explosion, how to provide tailored suggestions to a new user is a major concern for collaborative filtering (CF) based recommender systems. The CF recommender system performs very poorly for a new user with very poor profile information. Therefore, we investigate the use of quantitative association rules (QARs) for making recommendations to a new user by exploiting the cold user data which is readily available such as age, gender, occupation, etc. and ratings of items available in the historical data set. The proposed recommendation method, called QARF (QAR based filtering scheme), extracts relationships between readily available information of users and items, and the rating values. Additionally, QARs are extracted during offline processing which optimizes the online computation cost. The discovered rules are then employed during online processing in order to generate recommendations for a new user. Moreover, the QARF recommendation scheme is combined with CF, namely QARF/CF, to further improve recommendation accuracy. Proposed approaches QARF and QARF/CF are evaluated on the platform of MovieLens dataset. Experimental results demonstrate that the proposed schemes enhance new user recommendations and outperform other state of the art CF schemes.

Multidimensional and Quantitative Association Rule Mining from Remote Sensing Images

Data mining and knowledge discovery from a large amount of image data such as remote sensing images has become highly required recent years. The association rule discovery problem in particular has been widely studied. This paper presents preliminary work in using data mining techniques to find interesting multidimensional and quantitative association rule from remotely sensed data. The data concludes two variables (e.g. vegetation cover and farmland distribution) extracted from Landsat 7 ETM, slope (from DEM data) and along with additional data from USLE model (e.g. soil erosion ). Based on the characteristics of the remote sensed data, this article presents a method partitioning quantitative attributes into unequal partitions. We show one way to generate association rule is to transform the images' data into a set of market\|basket type transaction. The main advantage of doing this is that we can use the existing algorithm and software to discover the association rule that exist in the data. One example about soil and water erosion and closing farming land for forest or grasses is employed to support it.

Studying infant mortality rate: a data mining approach

The World Health Organization (WHO) uses a large number of health predicators to measure a country’s overall health status. Among many, Infant Mortality Rate (IMR) is considered to be one important. Current literature describes the importance of these predictors in a much segregated manner. Also, its interrelationships are underexplored. This paper describes a framework for mining the hidden relationships of some predictors, such as (i) Population Annual Growth Rate (PAG), (ii) Total Fertility Rate (TFR), (iii) General Government Expenditure (GGE), (iv) Social Security Expenditure (SSE), (v) Total Expenditure on Health (THE), (vi) Access of Drinking Water (ADW), and (vii) Access to Basic Sanitation (ABS) with IMR with the help of Quantitative Association Rule (QAR) mining technique. Public database of WHO has been accessed to gather the required data region and country-wise and then normalized [0, 1]. Association and correlation rules considering the pair-wise influences of the predictors on IMR and vice versa have been engineered. The quality of each association has been measured using the concept of ‘leverage’. While, the term ‘lift’ interprets a correlation. The study finally reveals that rules showing association correlations of PAG, GGE, TFR and ADW positively predict for IMR status.

Application of Cloud Theory in Association Rules

The data mining is to discover knowledge from the database, quantitative association rules mining method is difficult for their values are too large. The usual means is dividing quantitative Data to discrete conception. The Cloud model combines fuzziness and randomness organically, so it fits the real world objectively, a new method to mine association rules from quantitative data based on the cloud model was proposed, which first take the original data distribution in the database into account, and then use the trapezoidal cloud model to complicate concepts division, and transforms qualitative data to the quantitative concept, in the conversion take account of the basic characteristics of human behavior fully, divides quantitative Data with trapezium Cloud model to create discreet concepts, the concept cluster within one class, and separated with each other. So the quantitative Data can be transforms to Boolean data well, the Boolean data can be mined by the mature Boolean association rules mining method to find useful knowledge.

Quantitative Association Rule Mining on Weighted Transactional Data

In this paper we have proposed an approach for mining quantitative association rules. The aim of association rule mining is to find interesting and useful patterns from the transactional database. Its main application is in market basket analysis to identify patterns of items that are purchased together. Mining simple association rules involves less complexity and considers only the presence or absence of an item in a transaction. Quantitative association mining denotes association with itemsets and their quantities. To find such association rules involving quantity, we partition each item into equi-spaced bins with each bin representing a quantity range. Assuming each bin as a separate bin we proceed with mining and we also take care of reducing redundancies and rules between different bins of the same item. The algorithm is capable in generating association rules more close to real life situations as it considers the strength of presence of each item implicitly in the transactional data. Also the algorithm can be applied directly to real time data repositories to find association rules.

Analysis of the Effectiveness of the Genetic Algorithms based on Extraction of Association Rules

DataMining is most commonly used in attempts to induce association rules from transaction data which can help decision-makers easily analyze the data and make good decisions regarding the domains concerned. Most conventional studies are focused on binary or discrete-valued transaction data, however the data in real-world applications usually consists of quantitative values. In the last years, many researches have proposed Genetic Algorithms for mining interesting association rules from quantitative data. In this paper, we present a study of three genetic association rules extraction methods to show their effectiveness for mining quantitative association rules. Experimental results over two real-world databases are showed.

Discovery of Structural and Functional Features in RNA Pseudoknots

An RNA pseudoknot consists of nonnested double-stranded stems connected by single-stranded loops. There is increasing recognition that RNA pseudoknots are one of the most prevalent RNA structures and fulfill a diverse set of biological roles within cells, and there is an expanding rate of studies into RNA pseudoknotted structures as well as increasing allocation of function. These not only produce valuable structural data but also facilitate an understanding of structural and functional characteristics in RNA molecules. PseudoBase is a database providing structural, functional, and sequence data related to RNA pseudoknots. To capture the features of RNA pseudoknots, we present a novel framework using quantitative association rule mining to analyze the pseudoknot data. The derived rules are classified into specified association groups regarding structure, function, and category of RNA pseudoknots. The discovered association rules assist biologists in filtering out significant knowledge of structure-function and structure-category relationships. A brief biological interpretation to the relationships is presented, and their potential correlations with each other are highlighted.

URBAN CRIME ANALYSIS THROUGH AREAL CATEGORIZED MULTIVARIATE ASSOCIATIONS MINING

As geospatial data grows explosively, there is a great demand for the incorporation of data mining techniques into a geospatial context. Association rules mining is a core technique in data mining and is a solid candidate for the associative analysis of large geospatial databases. In this article, we propose a geospatial knowledge discovery framework for automating the detection of multivariate associations based on a given areal base map. We investigate a series of geospatial preprocessing steps involving data conversion and classification so that the traditional Boolean and quantitative association rules mining can be applied. Our framework has been integrated into GISs using a dynamic link library to allow the automation of both the preprocessing and data mining phases to provide greater ease of use for users. Experiments with real-crime datasets quickly reveal interesting frequent patterns and multivariate associations, which demonstrate the robustness and efficiency of our approach.

An information-theoretic approach to quantitative association rule mining

Quantitative association rule (QAR) mining has been recognized an influential research problem over the last decade due to the popularity of quantitative databases and the usefulness of association rules in real life. Unlike boolean association rules (BARs), which only consider boolean attributes, QARs consist of quantitative attributes which contain much richer information than the boolean attributes. However, the combination of these quantitative attributes and their value intervals always gives rise to the generation of an explosively large number of itemsets, thereby severely degrading the mining efficiency. In this paper, we propose an information-theoretic approach to avoid unrewarding combinations of both the attributes and their value intervals being generated in the mining process. We study the mutual information between the attributes in a quantitative database and devise a normalization on the mutual information to make it applicable in the context of QAR mining. To indicate the strong informative relationships among the attributes, we construct a mutual information graph (MI graph), whose edges are attribute pairs that have normalized mutual information no less than a predefined information threshold. We find that the cliques in the MI graph represent a majority of the frequent itemsets. We also show that frequent itemsets that do not form a clique in the MI graph are those whose attributes are not informatively correlated to each other. By utilizing the cliques in the MI graph, we devise an efficient algorithm that significantly reduces the number of value intervals of the attribute sets to be joined during the mining process. Extensive experiments show that our algorithm speeds up the mining process by up to two orders of magnitude. Most importantly, we are able to obtain most of the high-confidence QARs, whereas the QARs that are not returned by MIC are shown to be less interesting.

Efficient mining of salinity and temperature association rules from ARGO data

This paper presents an efficient technique for analyzing ARGO ocean data comprising time series of salinity/temperature measurements where informative salinity/temperature patterns are extracted. Most traditional mining techniques focus on finding associations among items within one transaction and are therefore unable to discover rich contextual patterns related to location and time. In order to show the associated salinity/temperature variations among different locations and time intervals, for example, “if the salinity rose from 0.15 psu to 0.25 psu in the area that is in the east–northeast direction and is near Taiwan, then the temperature will rise from 0 °C to 1.2 °C in the area that is in the east–northeast direction and is far away from Taiwan next month”, a quantitative inter-transaction association rules mining algorithm is proposed. The FITI and the PrefixSpan algorithms are adopted to maximize the mining efficiency. The strategy is applied to ocean salinity measurements obtained from the waters surrounding Taiwan. These experimental evaluations show that the proposed algorithm achieves better performance than other inter-transaction association rule mining algorithms.

Fuzzy versus quantitative association rules: a fair data-driven comparison

As opposed to quantitative association rule mining, fuzzy association rule mining is said to prevent the overestimation of boundary cases, as can be shown by small examples. Rule mining, however, becomes interesting in large databases, where the problem of boundary cases is less apparent and can be further suppressed by using sensible partitioning methods. A data-driven approach is used to investigate if there is a significant difference between quantitative and fuzzy association rules in large databases. The influence of the choice of a particular triangular norm in this respect is also examined.

An efficient algorithm for finding dense regions for mining quantitative association rules

Many algorithms have been proposed for mining boolean association rules. However, very little work has been done in mining quantitative association rules. Although we can transform quantitative attributes into boolean attributes, this approach is not effective and is difficult to scale up for high-dimensional cases and also may result in many imprecise association rules. Newly designed algorithms for quantitative association rules still are persecuted by the problems of nonscalability and noise. In this paper, an efficient algorithm, DRMiner, is proposed. By using the notion of “density” to capture the characteristics of quantitative attributes and an efficient procedure to locate the “dense regions”, DRMiner not only can solve the problems of previous approaches, but also can scale up well for high-dimensional cases. Evaluations on DRMiner have been performed using synthetic databases. The results show that DRMiner is effective and can scale up quite linearly with the increasing number of attributes.

An efficient genetic algorithm for automated mining of both positive and negative quantitative association rules

In this paper, a genetic algorithm (GA) is proposed as a search strategy for not only positive but also negative quantitative association rule (AR) mining within databases. Contrary to the methods used as usual, ARs are directly mined without generating frequent itemsets. The proposed GA performs a database-independent approach that does not rely upon the minimum support and the minimum confidence thresholds that are hard to determine for each database. Instead of randomly generated initial population, uniform population that forces the initial population to be not far away from the solutions and distributes it in the feasible region uniformly is used. An adaptive mutation probability, a new operator called uniform operator that ensures the genetic diversity, and an efficient adjusted fitness function are used for mining all interesting ARs from the last population in only single run of GA. The efficiency of the proposed GA is validated upon synthetic and real databases.

On the complexity of inducing categorical and quantitative association rules

Inducing association rules is one of the central tasks in data mining applications. Quantitative association rules induced from databases describe rich and hidden relationships to be found within data that can prove useful for various application purposes (e.g., market basket analysis, customer profiling, and others). Although association rules are quite widely used in practice, a thorough analysis of the related computational complexity is missing. This paper intends to provide a contribution in this setting. To this end, we first formally define quantitative association rule mining problems, which include boolean association rules as a special case; we then analyze computational complexity of such problems. The general problem as well as some interesting special cases are considered.

Generalized Fuzzy Quantitative Association Rules Mining with Fuzzy Generalization Hierarchies

Association rule mining is an exploratory learning task to discover some hidden dependency relationships among items in transaction data. Quantitative association rules denote association rules with both categorical and quantitative attributes. There have been several works on quantitative association rule mining such as the application of fuzzy techniques to quantitative association rule mining, the generalized association rule mining for quantitative association rules, and importance weight incorporation into association rule mining fer taking into account the users interest. This paper introduces a new method for generalized fuzzy quantitative association rule mining with importance weights. The method uses fuzzy concept hierarchies fer categorical attributes and generalization hierarchies of fuzzy linguistic terms fur quantitative attributes. It enables the users to flexibly perform the association rule mining by controlling the generalization levels for attributes and the importance weights f3r attributes.