Issue In Data Mining Research Articles

An Efficient Algorithm for Sliding Window-Based Weighted Frequent Pattern Mining over Data Streams

Traditional frequent pattern mining algorithms do not consider different semantic significances (weights) of the items. By considering different weights of the items, weighted frequent pattern (WFP) mining becomes an important research issue in data mining and knowledge discovery area. However, the existing state-of-the-art WFP mining algorithms consider all the data from the very beginning of a database to discover the resultant weighted frequent patterns. Therefore, their approaches may not be suitable for the large-scale data environment such as data streams where the volume of data is huge and unbounded. Moreover, they cannot extract the recent change of knowledge in a data stream adaptively by considering the old information which may not be interesting in the current time period. Another major limitation of the existing algorithms is to scan a database multiple times for finding the resultant weighted frequent patterns. In this paper, we propose a novel large-scale algorithm WFPMDS (Weighted Frequent Pattern Mining over Data Streams) for sliding window-based WFP mining over data streams. By using a single scan of data stream, the WFPMDS algorithm can discover important knowledge from the recent data elements. Extensive performance analyses show that our proposed algorithm is very efficient for sliding window-based WFP mining over data streams.

3DM: Domain-oriented Data-driven Data Mining

Recent developments in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for knowledge discovery large-scale database. Data mining technology is a useful tool for this task. It is an emerging area of computational intelligence that offers new theories, techniques, and tools for processing large volumes of data, such as data analysis, decision making, etc. There are countless researchers working on designing efficient data mining techniques, methods, and algorithms. Unfortunately,most data mining researchers pay much attention to technique problems for developing data mining models and methods, while little to basic issues of data mining. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? What is the rule we would obey in a data mining process? What is the relationship between the prior knowledge of domain experts and the knowledgemind from data? In this paper, we will address these basic issues of data mining from the viewpoint of informatics [1]. Data is taken as a manmade format for encoding knowledge about the natural world. We take data mining as a process of knowledge transformation. A domain-oriented data-driven data mining (3DM) model based on a conceptual data mining model is proposed. Some data-driven data mining algorithms are also proposed to show the validity of this model, e.g., the data-driven default rule generation algorithm, data-driven decision tree pre-pruning algorithm and data-driven knowledge acquisition from concept lattice.

Catalog segmentation with double constraints in business

Catalog segmentation is an important issue in data mining in business from the microeconomic point of view. In catalog segmentation, an enterprise tries to develop k catalogs with r products that are sent to corresponding customers in order to maximize the overall number of catalog products purchased. In this paper, a novel model called catalog segmentation problem with double constraints (DCCSP) is presented. In this model, the interest constraint is minimized and the profit constraint is maximized so that the profit of products purchased by customers who have at least t interesting products in receiving catalogs is maximized. The complexity of the DCCSP is analyzed, and a DCCS algorithm to solve the optimization is proposed. The experimental results show that the proposed algorithm is efficient and can be used to solve the DCCSP effectively.

Your Tax Dollars at Work! or The APPLES Trial Bears Fruit

Your Tax Dollars at Work! or The APPLES Trial Bears Fruit

Projected outlier detection in high-dimensional mixed-attributes data set

Detecting outlier efficiently is an active research issue in data mining, which has important applications in the field of fraud detection, network intrusion detection, monitoring criminal activities in electronic commerce, etc. Because of the sparsity of high dimensional data, it is reasonable and meaningful to detect the outliers in suitable projected subspaces. We call such subspace and outliers in the subspace as anomaly subspace and projected outlier respectively. Many efficient algorithms have already been proposed for outlier detection based on different approaches, but there are few literatures on projected outlier detection for high dimensional data sets with mixed continuous and categorical attributes. In this paper, a novel projected outlier detection algorithm is proposed to detect projected outliers in high-dimensional mixed attribute data set. Our main contributions are: (1) combined with information entropy, a novel measure of anomaly subspace is proposed. In this anomaly subspace, meaningful outliers could be detected and explained. Unlike the previous projected outlier detection methods, the dimension of anomaly subspace is not decided beforehand; (2) theoretical analysis about this measure is presented; (3) bottom-up method is proposed to find the interesting anomaly subspaces; (4) the outlying degree of projected outlier is defined, which has good explanations; (5) the data set with mixed data type is handled; (6) experiments on synthetic and real data sets to evaluate the effectiveness of our approach are performed.

Mining top- k Hot Melody Structures over online music query streams

Online mining of frequent patterns from music data is one of the most important research issues of multimedia data mining. Most previous studies require the specification of a min_ support threshold and aim at mining a complete set of frequent patterns satisfying min_ support. However, in practice, it is difficult for users to provide an appropriate value of min_ support threshold. In this paper, we propose a new problem of multimedia data mining: online mining of top- k melody structures of length no less than min_ l, where k is the desired number of hot melody structures to be mined and min_ l is the minimal length of each melody structure. An efficient single-pass algorithm, called top- k-HMS (top- k Hot Melody Structures), is developed for mining such melody structures without min_ support. In the framework of top- k-HMS algorithm, a new summary data structure, called TKM-list (top- k melody list) is developed to maintain the essential information about the top- k hot melody structures from the current melody sequence streams. Experimental studies show that the proposed top- k-HMS algorithm is an efficient one-pass method for mining the set of top- k Hot Melody Structures over a continuous stream of melody sequences.

Mining Weighted Association Rules without Preassigned Weights

Association rule mining is a key issue in data mining. However, the classical models ignore the difference between the transactions, and the weighted association rule mining does not work on databases with only binary attributes. In this paper, we introduce a new measure w-support, which does not require preassigned weights. It takes the quality of transactions into consideration using link-based models. A fast mining algorithm is given, and a large amount of experimental results are presented.

A new efficient probabilistic model for mining labeled ordered trees applied to glycobiology

Mining frequent patterns from large datasets is an important issue in data mining. Recently, complex and unstructured (or semi-structured) datasets have appeared as targets for major data mining applications, including text mining, web mining and bioinformatics. Our work focuses on labeled ordered trees, which are typically semi-structured datasets. In bioinformatics, carbohydrate sugar chains, or glycans, can be modeled as labeled ordered trees. Glycans are the third major class of biomolecules, having important roles in signaling and recognition. For mining labeled ordered trees, we propose a new probabilistic model and its efficient learning scheme which significantly improves the time and space complexity of an existing probabilistic model for labeled ordered trees. We evaluated the performance of the proposed model, comparing it with those of other probabilistic models, using synthetic as well as real datasets from glycobiology. Experimental results showed that the proposed model drastically reduced the computation time of the competing model, keeping the predictive power and avoiding overfitting to the training data. Finally, we assessed our results on real data from a variety of biological viewpoints, verifying known facts in glycobiology.

A Multi-criteria Convex Quadratic Programming model for credit data analysis

Speed and scalability are two essential issues in data mining and knowledge discovery. This paper proposed a mathematical programming model that addresses these two issues and applied the model to Credit Classification Problems. The proposed Multi-criteria Convex Quadric Programming (MCQP) model is highly efficient (computing time complexity O ( n 1.5–2 )) and scalable to massive problems (size of O (10 9 )) because it only needs to solve linear equations to find the global optimal solution. Kernel functions were introduced to the model to solve nonlinear problems. In addition, the theoretical relationship between the proposed MCQP model and SVM was discussed.

Attribute reduction based on evidence theory in incomplete decision systems

Attribute reduction is a basic issue in knowledge representation and data mining. This paper deals with attribute reduction in incomplete information systems and incomplete decision systems based on Dempster–Shafer theory of evidence. The concepts of plausibility reduct and belief reduct in incomplete information systems as well as relative plausibility reduct and relative belief reduct in incomplete decision systems are introduced. It is shown that in an incomplete information system an attribute set is a belief reduct if and only if it is a classical reduct and a plausibility consistent set must be a classical consistent set. In a consistent incomplete decision system, the concepts of relative reduct, relative plausibility reduct, and relative belief reduct are all equivalent. In an inconsistent incomplete decision system, an attribute set is a relative plausibility reduct if and only if it is a relative reduct, a plausibility consistent set must be a belief consistent set, and a belief consistent set is not a plausibility consistent set in general.

The New Physician as Unwitting Quantum Mechanic: Is Adapting Dirac's Inference System Best Practice for Personalized Medicine, Genomics, and Proteomics?

What is the Best Practice for automated inference in Medical Decision Support for personalized medicine? A known system already exists as Dirac's inference system from quantum mechanics (QM) using bra-kets <A | B> and bras <A| and kets |B> where A and B are states, events, or measurements representing, say, clinical and biomedical rules. Dirac's system should theoretically be the universal best practice for all inference, though QM is notorious as sometimes leading to bizarre conclusions that appear not to be applicable to the macroscopic world of everyday world human experience and medical practice. It is here argued that this apparent difficulty vanishes if QM is assigned one new multiplication function @, which conserves conditionality appropriately, making QM applicable to classical inference including a quantitative form of the predicate calculus. An alternative interpretation with the same consequences is if every i = radical-1 in Dirac's QM is replaced by h, an entity distinct from 1 and i and arguably a hidden root of 1 such that h2 = 1. With that exception, this paper is thus primarily a review of the application of Dirac's system, by application of linear algebra in the complex domain to help manipulate information about associations and ontology in complicated data. Any combined bra-ket <A | B> can be shown to be composed only of the sum of QM-like bra and ket weights c(<A|) and c(|B>), times an exponential function of Fano's mutual information measure I(A; B) about the association between A and B, that is, an association rule from data mining. With the weights and Fano measure re-expressed as expectations on finite data using Riemann's Incomplete (i.e., Generalized) Zeta Functions, actual counts of observations for real world sparse data can be readily utilized. Finally, the paper compares identical character, distinguishability of states events or measurements, correlation, mutual information, and orthogonal character, important issues in data mining and biomedical analytics, as in QM.

Data analysis and profiling

PurposeThis paper discusses the role and importance of data analysis and data mining within the context of direct marketing.Design/methodology/approachThe paper considers some of the prominent data analysis and data mining issues related to direct marketing. It has been designed to make those involved in direct marketing activities more cognizant of potential competitive advantages, and possible pitfalls, in the areas of data collection, analysis, and mining.FindingsIn order to be effective and successful, both strategically and tactically, a comprehensive data collection and analysis strategy must be incorporated into all aspects of direct marketing activities. This is illustrated through anecdotal evidence, industry sources, real‐life/real‐time examples, and a framework that can be employed by firms of any size.Originality/valueThe underlying value of the paper is that, beyond showing the importance of efficiently employing both existing and future data into the operational activities of direct marketing firms, it also provides guidelines as to how individual firms can establish a culture that enables the firm to use data as a means of leveraging competitive advantage resulting in increased growth and revenues.

Semi-parametric optimization for missing data imputation

Missing data imputation is an important issue in machine learning and data mining. In this paper, we propose a new and efficient imputation method for a kind of missing data: semi-parametric data. ...

A Model of Immune Gene Expression Programming for Rule Mining

Rule mining is an important issue in data mining. To address it, a novel Immune Gene Expression Programming (IGEP) model was proposed. Concepts of rule, gene, immune cell, and antibody were formalized. The dynamic evolution models and the corresponding recursive equations of immune cell, self, immune-tolerance were built. The novel key techniques of IGEP were presented. Experiment results showed that the new method has good stability, scalability and flexibility. It can discover traditional association rule, non-traditional rule including connective OR or NOT, and meta-rule of strong rule. Furthermore, it can perform well in constrained pattern mining.

Privacy-preserving support vector machine classification

Privacy is an important issue in the collaborative data mining since privacy concerns may prevent the parties from directly sharing the data and some types of information about the data. How multiple parties collaboratively conduct data mining without breaching data privacy presents a challenge. This paper seeks to investigate solutions for privacy-preserving support vector machine classification which is one of data mining tasks. The goal is to obtain accurate classification results without disclosing private data.

Technique without theory or theory from technique? An examination of practical, philosophical, and foundational issues in data mining

In this paper, it is argued that although data mining (DM) is being touted as a solution to many business problems and is basking in the glory of electronic business environments of today, as practiced currently, it reflects a preoccupation with short-run commercial applications and a neglect of the underlying theoretical issues. Although an argument can be made that theoretical precedence is not a necessary prerequisite for practical application or for commercial success, it can also be argued that an exclusive reliance on data-driven and exploratory components of pattern recognition without a corresponding attention to the causal schemas underlying patterns is destined to limit the potential for DM to evolve into a long-term solution to business problems or into an intellectual discipline in its own right. This paper presents an overview of key features and assumptions in DM and examines some of the key practical, philosophical, and foundational issues in DM.

A minimal perfect hashing scheme to mining association rules from frequently updated data

Data mining techniques have attracted the attention of researchers from various areas. One of the most important issues in data mining is the mining of association rules from very large and frequently updated databases. In this paper, an algorithm for mining association rules based on a minimal perfect hashing scheme is presented. By generating non‐collision hashing tables, the novel approach is especially suitable for handling very large databases containing huge amounts of transactions and frequently updated data. Some experiments have been performed on the databases with the number of transactions ranging from 10,000 to 1,000,000. The experimental result shows that our novel approach performs much better than previously proposed methods.

A fast distributed algorithm for association rule mining based on binary coding mapping relation

Association rule mining is an important issue in data mining. The paper proposed an binary system based method to generate candidate frequent itemsets and corresponding supporting counts efficiently, which needs only some operations such as “and”, “or” and “xor”. Applying this idea in the existed distributed association rule mining algorithm FDM, the improved algorithm BFDM is proposed. The theoretical analysis and experiment testify that BFDM is effective and efficient.

Identifying noisy features with the Pairwise Attribute Noise Detection Algorithm

A critical issue in data mining and knowledge discovery is the problem of data quality. Quantifying the presence of noise in dataset is often used as an indicator of data quality. While existing works have mostly focused on detecting class noise or mislabeling errors, very limited attention has been given to finding noisy attributes or features. Prior work in the area of noise handling has concentrated on the detection of observations that contain noise in either the attributes or class labels. Methodologies that provide insight into the quality of an attribute can provide valuable knowledge to a domain expert when data analysis is being performed. We present a novel methodology for detecting noisy attributes. The procedure utilizes our recently proposed Pairwise Attribute Noise Detection Algorithm (PANDA) for detecting instances with attribute noise. From a data analyst's point of view, our approach provides a viable solution to: Given a dataset, which attribute(s) contains the most noise?. The proposed methodology is investigated with multiple case studies of a real-world software measurement dataset. The empirical study is investigated by injecting simulated noise into one or more attributes of a dataset that has no class noise. Based on a domain expert's inspection of the obtained results, the effectiveness of our technique for detecting noisy attributes is demonstrated.

Association rules mining in vertically partitioned databases

Privacy concerns have become an important issue in Data Mining. This paper deals with the problem of association rule mining from distributed vertically partitioned data with the goal of preserving the confidentiality of each database. Each site holds some attributes of each transaction, and the sites wish to work together to find globally valid association rules without revealing individual transaction data. This problem occurs, for example, when the same users access several electronic shops purchasing different items in each. We present two algorithms for discovering frequent itemsets and for calculating the confidence of the rules. We then analyze the algorithms privacy properties, and compare them to other published algorithms.