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IEEE ACCESS 2019年 7卷 19954-19964页

作者： Su, Peng Zeng, Daniel Zhao, Huimin Dali Univ Sch Math & Comp Sci Dali 671003 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Intelligent Control & Management Co Beijing 100190 Peoples R China Univ Wisconsin Sheldon B Lubar Sch Business Milwaukee WI 53201 USA

The actionable behavioral rules suggest specific actions that may influence certain behavior in the stakeholders' best interest. In mining such rules, it was assumed previously that all attributes are categorical while the numerical attributes have been discretized in advance. However, this assumption significantly reduces the solution space, and thus hinders the potential of mining algorithms, especially when the numerical attributes are prevalent. As the numerical data are ubiquitous in business applications, there is a crucial need for new mining methodologies that can better leverage such data. To meet this need, in this paper, we define a new data mining problem, named behavior action mining, as a problem of continuous variable optimization of expected utility for action. We then develop three approaches to solving this new problem, which uses regression as a technical basis. The experimental results based on a marketing dataset demonstrate the validity and superiority of our proposed approaches.

关键词： Business decision support knowledge and data engineering tools and techniques mining methods and algorithms

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Anonymizing graphs: measuring quality for clustering

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KNOWLEDGE AND INFORMATION SYSTEMS 2015年第3期44卷 507-528页

作者： Casas-Roma, Jordi Herrera-Joancomarti, Jordi Torra, Vicenc UOC Barcelona Spain Univ Autonoma Barcelona Bellaterra Spain CSIC Artificial Intelligence Res Inst IIIA Bellaterra Spain

Anonymization of graph-based data is a problem, which has been widely studied last years, and several anonymization methods have been developed. Information loss measures have been carried out to evaluate the noise introduced in the anonymized data. Generic information loss measures ignore the intended anonymized data use. When data has to be released to third-parties, and there is no control on what kind of analyses users could do, these measures are the standard ones. In this paper we study different generic information loss measures for graphs comparing such measures to the cluster-specific ones. We want to evaluate whether the generic information loss measures are indicative of the usefulness of the data for subsequent data mining processes.

关键词： Privacy Networks Data mining mining methods and algorithms Quality and Metrics Semi-structured data and XML

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Automated Hierarchical Density Shaving: A Robust Automated Clustering and Visualization Framework for Large Biological Data Sets

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2010年第2期7卷 223-237页

作者： Gupta, Gunjan Liu, Alexander Ghosh, Joydeep Amazon Com Seattle WA 98114 USA Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA

A key application of clustering data obtained from sources such as microarrays, protein mass spectroscopy, and phylogenetic profiles is the detection of functionally related genes. Typically, only a small number of functionally related genes cluster into one or more groups, and the rest need to be ignored. For such situations, we present Automated Hierarchical Density Shaving (Auto-HDS), a framework that consists of a fast hierarchical density-based clustering algorithm and an unsupervised model selection strategy. Auto-HDS can automatically select clusters of different densities, present them in a compact hierarchy, and rank individual clusters using an innovative stability criteria. Our framework also provides a simple yet powerful 2D visualization of the hierarchy of clusters that is useful for further interactive exploration. We present results on Gasch and Lee microarray data sets to show the effectiveness of our methods. Additional results on other biological data are included in the supplemental material.

关键词： mining methods and algorithms data and knowledge visualization clustering bioinformatics

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Parallel Frequent Item Set mining with Selective Item Replication

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2011年第10期22卷 1632-1640页

作者： Ozkural, Eray Ucar, Bora Aykanat, Cevdet Bilkent Univ Dept Comp Engn TR-06800 Ankara Turkey ENS Lyon LIP F-69364 Lyon France

We introduce a transaction database distribution scheme that divides the frequent item set mining task in a top-down fashion. Our method operates on a graph where vertices correspond to frequent items and edges correspond to frequent item sets of size two. We show that partitioning this graph by a vertex separator is sufficient to decide a distribution of the items such that the subdatabases determined by the item distribution can be mined independently. This distribution entails an amount of data replication, which may be reduced by setting appropriate weights to vertices. The data distribution scheme is used in the design of two new parallel frequent item set mining algorithms. Both algorithms replicate the items that correspond to the separator. NoClique replicates the work induced by the separator and NoClique2 computes the same work collectively. Computational load balancing and minimization of redundant or collective work may be achieved by assigning appropriate load estimates to vertices. The experiments show favorable speedups on a system with small-to-medium number of processors for synthetic and real-world databases.

关键词： Parallel data mining frequent item set mining mining methods and algorithms selective data replication graph partitioning by vertex separator

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Efficiently mining gene expression data via a novel parameterless clustering method

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2005年第4期2卷 355-365页

作者： Tseng, VS Kao, CP Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan 701 Taiwan

Clustering analysis has been an important research topic in the machine learning field due to the wide applications. In recent years, it has even become a valuable and useful tool for in-silico analysis of microarray or gene expression data. Although a number of clustering methods have been proposed, they are confronted with difficulties in meeting the requirements of automation, high quality, and high efficiency at the same time. In this paper, we propose a novel, parameterless and efficient clustering algorithm, namely, Correlation Search Technique (CST), which fits for analysis of gene expression data. The unique feature of CST is it incorporates the validation techniques into the clustering process so that high quality clustering results can be produced on the fly. Through experimental evaluation, CST is shown to outperform other clustering methods greatly in terms of clustering quality, efficiency, and automation on both of synthetic and real data sets.

关键词： machine learning data mining clustering mining methods and algorithms

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TFP: An efficient algorithm for mining top-K frequent closed itemsets

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2005年第5期17卷 652-664页

作者： Wang, JY Han, JW Lu, Y Tzvetkov, P Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Univ Illinois Dept Comp Sci Siebel Ctr Sci 2132 Urbana IL 61801 USA

Frequent itemset mining has been studied extensively in literature. Most previous studies require the specification of a min_support threshold and aim at mining a complete set of frequent itemsets satisfying min_support. However, in practice, it is difficult for users to provide an appropriate min_support threshold. In addition, a complete set of frequent itemsets is much less compact than a set of frequent closed itemsets. In this paper, we propose an alternative mining task: mining top-k frequent closed itemsets of length no less than min_l, where k is the desired number of frequent closed itemsets to be mined, and min_l is the minimal length of each itemset. An efficient algorithm, called TFP, is developed for mining such itemsets without mins_support. Starting at min_support = 0 and by making use of the length constraint and the properties of top-k frequent closed itemsets, min_support can be raised effectively and FP-Tree can be pruned dynamically both during and after the construction of the tree using our two proposed methods: the closed node count and descendant_sum. Moreover, mining is further speeded up by employing a top-down and bottom-up combined FP-Tree traversing strategy, a set of search space pruning methods, a fast 2-level hash-indexed result tree, and a novel closed itemset verification scheme. Our extensive performance study shows that TFP has high performance and linear scalability in terms of the database size.

关键词： data mining frequent itemset association rules mining methods and algorithms

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Pattern-growth based frequent serial episode discovery

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DATA & KNOWLEDGE ENGINEERING 2013年 87卷 91-108页

作者： Achar, Avinash Ibrahim, A. Sastry, P. S. Indian Inst Sci Dept Elect Engn Bangalore 560012 Karnataka India

Frequent episode discovery is a popular framework for pattern discovery from sequential data. It has found many applications in domains like alarm management in telecommunication networks, fault analysis in the manufacturing plants, predicting user behavior in web click streams and so on. In this paper, we address the discovery of serial episodes. In the episodes context, there have been multiple ways to quantify the frequency of an episode. Most of the current algorithms for episode discovery under various frequencies are apriori-based level-wise methods. These methods essentially perform a breadth-first search of the pattern space. However currently there are no depth-first based methods of pattern discovery in the frequent episode framework under many of the frequency definitions. In this paper, we try to bridge this gap. We provide new depth-first based algorithms for serial episode discovery under non-overlapped and total frequencies. Under non-overlapped frequency, we present algorithms that can take care of span constraint and gap constraint on episode occurrences. Under total frequency we present an algorithm that can handle span constraint. We provide proofs of correctness for the proposed algorithms. We demonstrate the effectiveness of the proposed algorithms by extensive simulations. We also give detailed run-time comparisons with the existing apriori-based methods and illustrate scenarios under which the proposed pattern-growth algorithms perform better than their apriori counterparts. (C) 2013 Elsevier B.V. All rights reserved.

关键词： mining methods and algorithms Frequent episodes Non-overlapped frequency Span constraints Gap constraints Depth-first search

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Identifying At-Risk Students for Early Interventions-A Time-Series Clustering Approach

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2017年第1期5卷 45-55页

作者： Hung, Jui-Long Wang, Morgan C. Wang, Shuyan Abdelrasoul, Maha Li, Yaohang He, Wu Boise State Univ Dept Educ Technol Boise ID 83725 USA Univ Cent Florida Dept Stat Orlando FL 32816 USA Univ Southern Mississippi Dept Curriculum Instruct & Special Educ Hattiesburg MS 39406 USA Old Dominion Univ Dept Comp Sci Norfolk VA 23529 USA Old Dominion Univ Dept Informat Technol & Decis Sci Norfolk VA 23529 USA

The purpose of this paper is to identify at-risk online students earlier, more often, and with greater accuracy using time-series clustering. The case study showed that the proposed approach could generate models with higher accuracy and feasibility than the traditional frequency aggregation approaches. The best performing model can start to capture at-risk students from week 10. In addition, the four phases in student's learning process detected holiday effect and illustrate at-risk students' behaviors before and after a long holiday break. The findings also enable online instructors to develop corresponding instructional interventions via course design or student-teacher communications.

关键词： Clustering classification and association rules feature extraction or construction mining methods and algorithms time-series analysis LMS predictive modeling

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Target-Based, Privacy Preserving, and Incremental Association Rule mining

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2017年第4期10卷 633-645页

作者： Ahluwalia, Madhu V. Gangopadhyay, Aryya Chen, Zhiyuan Yesha, Yelena Harris Corp 2235 Monroe St Herndon VA 20171 USA Univ Maryland Baltimore Cty Dept Informat Syst 1000 Hilltop Circle Baltimore MD 21250 USA Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn 1000 Hilltop Circle Baltimore MD 21250 USA

We consider a special case in association rule mining where mining is conducted by a third party over data located at a central location that is updated from several source locations. The data at the central location is at rest while that flowing in through source locations is in motion. We impose some limitations on the source locations, so that the central target location tracks and privatizes changes and a third party mines the data incrementally. Our results show high efficiency, privacy and accuracy of rules for small to moderate updates in large volumes of data. We believe that the framework we develop is therefore applicable and valuable for securely mining big data.

关键词： Association rules data mining mining methods and algorithms security integrity protection

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Using fixed point theorems to model the binding in protein-protein interactions

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2005年第8期17卷 1079-1087页

作者： Li, JY Li, HQ Inst Infocomm Res Singapore 119613 Singapore

The binding in protein-protein interactions exhibits a kind of biochemical stability in cells. The mathematical notion of fixed points also describes stability. A point is a fixed point if it remains unchanged after a transformation by a function. Many points may not be a fixed point, but they may approach a stable status after multiple steps of transformation. In this paper, we define a point as a protein motif pair consisting of two traditional protein motifs. We propose a function and propose a method to discover stable motif pairs of this function from a large protein interaction sequence data set. There are many interesting properties for this function (for example, the convergence). Some of them are useful for gaining much efficiency in the discovery of those stable motif pairs;some are useful for explaining why our proposed fixed point theorems are a good way to model the binding of protein interactions. Our results are also compared to biological results to elaborate the effectiveness of our method.

关键词： bioinformatics (genome or protein) database mining methods and algorithms generating functions stability and instability biology and genetics

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