Finding Soft Relations in Granular Information Hierarchies

Various sensor network measurement studies have reported instances of transient faults in sensor readings. In this work, we seek to answer a simple question: How often are such faults observed in real deployments? To do this, we first explore and characterize three qualitatively different classes of fault detection methods. Rule-based methods leverage domain knowledge to develop heuristic rules for detecting and identifying faults. Estimation methods predict "normal" sensor behavior by leveraging sensor correlations, flagging anomalous sensor readings as faults. Finally, learning-based methods are trained to statistically identify classes of faults. We find that these three classes of methods sit at different points on the accuracy/robustness spectrum. Rule-based methods can be highly accurate, but their accuracy depends critically on the choice of parameters. Learning methods can be cumbersome, but can accurately detect and classify faults. Estimation methods are accurate, but cannot classify faults. We apply these techniques to four real-world sensor data sets and find that the prevalence of faults as well as their type varies with data sets. All three methods are qualitatively consistent in identifying sensor faults in real world data sets, lending credence to our observations. Our work is a first-step towards automated on-line fault detection and classification.

The solution space of a frequent itemset generally presents exponential explosive growth because of the high-dimensional attributes of big data. However, the premise of the big data association rule analysis is to mine the frequent itemset in high-dimensional transaction sets. Traditional and classical algorithms such as the Apriori and FP-Growth algorithms, as well as their derivative algorithms, are unacceptable in practical big data analysis in an explosive solution space because of their huge consumption of storage space and running time. A multi-objective optimization algorithm was proposed to mine the frequent itemset of high-dimensional data. First, all frequent 2-itemsets were generated by scanning transaction sets based on which new items were added in as the objects of population evolution. Algorithms aim to search for the maximal frequent itemset to gather more non-void subsets because non-void subsets of frequent itemsets are all properties of frequent itemsets. During the operation of algorithms, lethal gene fragments in individuals were recorded and eliminated so that individuals may resurge. Finally, the set of the Pareto optimal solution of the frequent itemset was gained. All non-void subsets of these solutions were frequent itemsets, and all supersets are non-frequent itemsets. Finally, the practicability and validity of the proposed algorithm in big data were proven by experiments.

Data mining and knowledge discovery aim at finding useful information from typically massive collections of data, and then extracting useful knowledge from the information. To date a large number of approaches have been proposed to find useful information and discover useful knowledge; for example, decision trees, Bayesian belief networks, evidence theory, rough set theory, fuzzy set theory, kNN (k-nearest-neighborhood) classifier, neural networks, and support vector machines. However, these approaches are based on a specific data type. In the real world, an intelligent system often encounters mixed data types, incomplete information (missing values), and imprecise information (fuzzy conditions). In the UCI (University of California – Irvine) Machine Learning Repository, it can be seen that there are many real world data sets with missing values and mixed data types. It is a challenge to enable machine learning or data mining approaches to deal with mixed data types (Ching, 1995; Coppock, 2003) because there are difficulties in finding a measure of similarity between objects with mixed data type attributes. The problem with mixed data types is a long-standing issue faced in data mining. The emerging techniques targeted at this issue can be classified into three classes as follows: (1) Symbolic data mining approaches plus different discretizers (e.g., Dougherty et al., 1995; Wu, 1996; Kurgan et al., 2004; Diday, 2004; Darmont et al., 2006; Wu et al., 2007) for transformation from continuous data to symbolic data; (2) Numerical data mining approaches plus transformation from symbolic data to numerical data (e.g.,, Kasabov, 2003; Darmont et al., 2006; Hadzic et al., 2007); (3) Hybrid of symbolic data mining approaches and numerical data mining approaches (e.g.,, Tung, 2002; Kasabov, 2003; Leng et al., 2005; Wu et al., 2006). Since hybrid approaches have the potential to exploit the advantages from both symbolic data mining and numerical data mining approaches, this chapter, after discassing the merits and shortcomings of current approaches, focuses on applying Self-Organizing Computing Network Model to construct a hybrid system to solve the problems of knowledge discovery from databases with a diversity of data types. Future trends for data mining on mixed type data are then discussed. Finally a conclusion is presented.

An important task in knowledge discovery is feature selection. This paper describes a practical approach to feature subset selection proposed as part of a hybrid rough sets/neural network framework for knowledge discovery for decision support. In this framework neural networks and rough sets are combined and used cooperatively during the system life cycle. The reason for combining rough sets with neural networks in the proposed framework is twofold. Firstly, rough sets based systems provide domain knowledge expressed in the form of If-then rules as well as tools for data analysis. Secondly, rough sets are used in this framework in the task of feature selection for neural network models. This paper examines the feature selection aspect of the framework. An empirical study that tested the approach on artificial datasets and real-world datasets was carried out. Experimental results indicate that the proposed approach can improve the performance of neural network models. The framework was also applied in the development of a real-world decision support system. The experience with this application has shown that the approach can support the users in the task of feature selection.

Among the methods proposed for identifying the number of latent traits in multidimensional IRT models, DETECT has attracted the attention of both methodologists and applied researchers as a nonparametric counterpart to other procedures. The current study investigated the overall performance of the DETECT procedure and its outcomes using a real-data sampling design recommended by MacCallum (2003) and compared the results from a purely simulated data set that was generated with a well-specified “perfect” model. The comparison revealed that the sampling behavior of the maximized DETECT value and R-ratio statistics was quite robust to minor factors and other model misspecifications that potentially exist in the real data set, as there were negligible differences between the results of the real and simulated data sets. Item classification accuracy was also nearly identical for the real and simulated data sets. The accuracy of the identified number of dimensions reported by DETECT was the only outcome with an obvious difference between the purely simulated data set and the real data set. While the difference was small for smaller sample sizes, the identified number of dimensions was more accurate for larger sample sizes when the population data set was purely simulated. In many instances, exploratory DETECT analysis outperformed the cross-validated DETECT analysis in terms of overall accuracy.

Extensive Copy-Number Variation of the Human Olfactory Receptor Gene Family

Conventional clustering algorithms categorize an object into precisely one cluster. In many applications, the membership of some of the objects to a cluster can be ambiguous. Therefore, an ability to specify membership to multiple clusters can be useful in real world applications. Fuzzy clustering makes it possible to specify the degree to which a given object belongs to a cluster. In Rough set representations, an object may belong to more than one cluster, which is more flexible than the conventional crisp clusters and less verbose than the fuzzy clusters. The unsupervised nature of fuzzy and rough algorithms means that there is a choice about the level of precision depending on the choice of parameters. This paper describes how one can vary the precision of the rough set clustering and studies its effect on synthetic and real world data sets.

We explore a new problem of mining general temporal association rules in publication databases. In essence, a publication database is a set of transactions where each transaction T is a set of items of which each item contains an individual exhibition period. The current model of association rule mining is not able to handle the publication database due to the following fundamental problems, i.e., 1) lack of consideration of the exhibition period of each individual item and 2) lack of an equitable support counting basis for each item. To remedy this, we propose an innovative algorithm progressive-partition-miner (abbreviated as PPM) to discover general temporal association rules in a publication database. The basic idea of PPM is to first partition the publication database in light of exhibition periods of items and then progressively accumulate the occurrence count of each candidate 2-itemset based on the intrinsic partitioning characteristics. Algorithm PPM is also designed to employ a filtering threshold in each partition to early prune out those cumulatively infrequent 2-itemsets. The feature that the number of candidate 2-itemsets generated by PPM is very close to the number of frequent 2-itemsets allows us to employ the scan reduction technique to effectively reduce the number of database scans. Explicitly, the execution time of PPM is, in orders of magnitude, smaller than those required by other competitive schemes that are directly extended from existing methods. The correctness of PPM is proven and some of its theoretical properties are derived. Sensitivity analysis of various parameters is conducted to provide many insights into Algorithm PPM.

Problem statement: The processing of the XML queries needs an efficient indexing method. The index generation includes the labeling of the nodes. But for the dynamic data, when the changes are made, the re-computation of the labeling is needed. Approach: Based on the structural indexing used the performance of the query processing system will be affected. The dynamic XML document allows inserting, deleting and updating operations. Results: Suppose for the frequently changed documents, the indexes will also be changed. i.e., it requires the re-computing the labels frequently. This leads to an inconvenient system. To avoid this problem, in the scheme New Labeling Scheme for XML (NLSX), only the small, capital letters and digits are used to generate persistent labels for the nodes in the document. In the proposed system New Labeling Scheme for XML using Unicode Characters, characters from Unicode Characters (NLSXU) are used. Thus it can provide more combinations of the characters for persistent labeling the nodes in the document so that it will very much reduce the space needed to store the labels. Using the proposed scheme NLSXU, the index size of the real world data sets will be greatly reduced by 81% of the existing scheme NLSX. The results shows that the proposed scheme NLSXU will reduce the size of the indexes of the synthetic data sets up to 26, 34, 71 and 95% than the NLSX, LSDX, GRP and SP schemes respectively. Also when compared to LSDX scheme, the NLSXU will reduce the time taken for generating the labels by 96 and 80% for the real world datasets and the synthetic data sets respectively. Conclusion: Finally when compared to NLSX scheme, the NLSXU will reduce the time taken for generating the labels by 66 and 15% for the real world datasets and the synthetic data sets respectively. Thus it will improve the performance of the query system.

The optimization of queries is critical in database management systems and the complexity involved in finding optimal solutions has led to the development of heuristic approaches. Answering data mining query involves a random search over large databases. Due to the enormity of the data set involved, model simplification is necessary for quick answering of data mining queries. In this chapter, we propose a hybrid model using rough sets and genetic algorithms for fast and efficient query answering. Rough sets are used to classify and summarize the datasets, whereas genetic algorithms are used for answering association related queries and feedback for adaptive classification. Here, we consider three types of queries, i.e., select, aggregate and classification based data mining queries. Summary tables that are built using rough sets and analytical model of attributes are used to speed up select queries. Mining associations, building concept hierarchies and reinforcement of reducts are achieved through genetic algorithms. The experiments are conducted on three real-life data sets, which include KDD 99 Cup data, Forest Cover-type data and Iris data. The performance of the proposed algorithm is analyzed for both execution time and classification accuracy and the results obtained are good.

Matrix approach to decision-theoretic rough sets for evolving data

We introduce a new method for feature extraction from object data that is based on the idea of preserving metric topology between the original and derived data sets. Specifically, our method attempts to produce neighbors in the derived data that have the same ranks as in the input data. The algorithm we propose is a novel modification of particle swarm optimization that involves multiswarms. We compare our model and algorithm to feature extraction using Sammon's method and principal components analysis on 19 data sets: 17 are created by making draws from p-variate Gaussian distributions. We also use two real world data sets - the Glass and Lung Cancer data available at the UCI ML website. We find that the new method compares well with Sammon's method, and seems to be superior to features derived with principal components analysis.

Forensic analysis commonly involves searching an investigation target for personal identifiable information. An Inland Revenue Department (IRD) number is used for taxation purposes in New Zealand and can provide evidence of perpetrator identity, transaction information or electronic fraud. This research has designed and implemented a bulk_extractor feature scanner to detect and validate IRD numbers (features). The IRD scanner has been tested on a known data set to ensure tool functionality. A large real world data set was then used to determine scanner effectiveness in a realistic investigation scenario. Real world data set testing highlighted a high number of unrelated features detected by the scanner. To combat this, a novel post-processing technique was implemented to identify forensically interesting IRD numbers by performing feature context searching. The post-processing findings proved that feature context searching is an effective data reduction technique that identified a low number of directly relevant IRD numbers.

Researchers in science and engineering face various obstacles due to a lack of specific and full data. Many different approaches have been devised to deal with these restrictive requirements, but two notable schools of thought are the fuzzy set (FS) theory and the rough set (RS) theory, both of which have spawned many extensions and hybridizations. Although RS theory originated from an indiscernibility relation (also known as an equivalence relation in mathematics), emphasis rapidly shifted to similarity or coverings (and their fuzzy analogues). Many other hybrid schemes were suggested with this goal in mind. The gap between those concepts shrank because to this thorough analysis. Fuzzy set theory is a legitimate way to convey the ambiguity of assessment data, yet it is still inadequate for dealing with certain intricate problems in the actual world. In reality, decision makers will undoubtedly provide different kinds of ambiguous and nuanced assessments. Atanassov’s intuitionistic fuzzy set theory broadened the application of fuzzy set theory by imbuing it with an element of uncertainty. Sometimes in real life, you have to deal with a neutral element on top of the indeterminate one. Picture fuzzy sets were developed specifically for this purpose. Membership roles may be positive, neutral, or negative/refusal. In contrast, hesitant fuzzy sets and its hybrid models are useful when decision makers are on the fence about which option to choose. As a binary relation on a set, a graph is symmetric. It is a staple in mathematical modelling and is used in almost every scientific and technological discipline. Graph theory has been essential in the mathematical modelling and subsequent resolution of several real-world situations. Information about connections between things is often best represented using graph theory, which uses vertices to stand in for the items and edges for the relationships between them. The suggested dynamic algorithm is better to the static approach in dealing with the multidimensional dynamic changes of the hybrid incomplete decision system, according to a series of experiments carried out on nine UCI datasets.

SufRec, an algorithm for mining association rules: Recursivity and task parallelism