采用限制与多维技术的数据采掘

针对当今数据采掘中效率不够高的问题，提出了采用限制与多维技术来进行数据采掘，讨论了哪些种类的限制能运用到采掘过程中，设计了一个数据采掘系统结构。     

A constraint-based querying system for exploratory pattern discovery

In this article we present ConQueSt, a constraint-based querying system able to support the intrinsically exploratory (i.e., human-guided, interactive and iterative) nature of pattern discovery. Following the inductive database vision, our framework provides users with an expressive constraint-based query language, which allows the discovery process to be effectively driven toward potentially interesting patterns. Such constraints are also exploited to reduce the cost of pattern mining computation. ConQueSt is a comprehensive mining system that can access real-world relational databases from which to extract data. Through the interaction with a friendly graphical user interface (GUI), the user can define complex mining queries by means of few clicks. After a pre-processing step, mining queries are answered by an efficient and robust pattern mining engine which entails the state-of-the-art of data and search space reduction techniques. Resulting patterns are then presented to the user in a pattern browsing window, and possibly stored back in the underlying database as relations.     

Itemset mining: A constraint programming perspective

The field of data mining has become accustomed to specifying constraints on patterns of interest. A large number of systems and techniques has been developed for solving such constraint-based mining problems, especially for mining itemsets. The approach taken in the field of data mining contrasts with the constraint programming principles developed within the artificial intelligence community. While most data mining research focuses on algorithmic issues and aims at developing highly optimized and scalable implementations that are tailored towards specific tasks, constraint programming employs a more declarative approach. The emphasis lies on developing high-level modeling languages and general solvers that specify what the problem is, rather than outlining how a solution should be computed, yet are powerful enough to be used across a wide variety of applications and application domains.This paper contributes a declarative constraint programming approach to data mining. More specifically, we show that it is possible to employ off-the-shelf constraint programming techniques for modeling and solving a wide variety of constraint-based itemset mining tasks, such as frequent, closed, discriminative, and cost-based itemset mining. In particular, we develop a basic constraint programming model for specifying frequent itemsets and show that this model can easily be extended to realize the other settings. This contrasts with typical procedural data mining systems where the underlying procedures need to be modified in order to accommodate new types of constraint, or novel combinations thereof. Even though the performance of state-of-the-art data mining systems outperforms that of the constraint programming approach on some standard tasks, we also show that there exist problems where the constraint programming approach leads to significant performance improvements over state-of-the-art methods in data mining and as well as to new insights into the underlying data mining problems. Many such insights can be obtained by relating the underlying search algorithms of data mining and constraint programming systems to one another. We discuss a number of interesting new research questions and challenges raised by the declarative constraint programming approach to data mining.     

Constraint-based sequential pattern mining: the pattern-growth methods

Constraints are essential for many sequential pattern mining applications. However, there is no systematic study on constraint-based sequential pattern mining. In this paper, we investigate this issue and point out that the framework developed for constrained frequent-pattern mining does not fit our mission well. An extended framework is developed based on a sequential pattern growth methodology. Our study shows that constraints can be effectively and efficiently pushed deep into the sequential pattern mining under this new framework. Moreover, this framework can be extended to constraint-based structured pattern mining as well. This research is supported in part by NSERC Grant 312194-05, NSF Grants IIS-0308001, IIS-0513678, BDI-0515813 and National Science Foundation of China (NSFC) grants No. 60303008 and 69933010. All opinions, findings, conclusions and recommendations in this paper are those of the authors and do not necessarily reflect the views of the funding agencies.     

Visual data mining in large geospatial point sets

Visual data-mining techniques have proven valuable in exploratory data analysis, and they have strong potential in the exploration of large databases. Detecting interesting local patterns in large data sets is a key research challenge. Particularly challenging today is finding and deploying efficient and scalable visualization strategies for exploring large geospatial data sets. One way is to share ideas from the statistics and machine-learning disciplines with ideas and methods from the information and geo-visualization disciplines. PixelMaps in the Waldo system demonstrates how data mining can be successfully integrated with interactive visualization. The increasing scale and complexity of data analysis problems require tighter integration of interactive geospatial data visualization with statistical data-mining algorithms.     

A hybrid cost estimation framework based on feature-oriented data mining approach

This paper presents an informatics framework to apply feature-based engineering concept for cost estimation supported with data mining algorithms. The purpose of this research work is to provide a practical procedure for more accurate cost estimation by using the commonly available manufacturing process data associated with ERP systems. The proposed method combines linear regression and data-mining techniques, leverages the unique strengths of the both, and creates a mechanism to discover cost features. The final estimation function takes the user’s confidence level over each member technique into consideration such that the application of the method can phase in gradually in reality by building up the data mining capability. A case study demonstrates the proposed framework and compares the results from empirical cost prediction and data mining. The case study results indicate that the combined method is flexible and promising for determining the costs of the example welding features. With the result comparison between the empirical prediction and five different data mining algorithms, the ANN algorithm shows to be the most accurate for welding operations.     

Mining fuzzy association rules in a bank-account database

This paper describes how we applied a fuzzy technique to a data-mining task involving a large database that was provided by an international bank with offices in Hong Kong. The database contains the demographic data of over 320,000 customers and their banking transactions, which were collected over a six-month period. By mining the database, the bank would like to be able to discover interesting patterns in the data. The bank expected that the hidden patterns would reveal different characteristics about different customers so that they could better serve and retain them. To help the bank achieve its goal, we developed a fuzzy technique, called fuzzy association rule mining II (FARM II). FARM II is able to handle both relational and transactional data. It can also handle fuzzy data. The former type of data allows FARM II to discover multidimensional association rules, whereas the latter data allows some of the patterns to be more easily revealed and expressed. To effectively uncover the hidden associations in the bank-account database, FARM II performs several steps which are described in detail in this paper. With FARM II, the bank discovered that they had identified some interesting characteristics about the customers who had once used the bank's loan services but then decided later to cease using them. The bank translated what they discovered into actionable items by offering some incentives to retain their existing customers.     

An agent-based hybrid framework for database mining

While knowledge discovery in databases (KDD) is defined as an iterative sequence of the following steps: data pre-processing, data mining, and post data mining, a significant amount of research in data mining has been done, resulting in a variety of algorithms and techniques for each step. However, a single data-mining technique has not been proven appropriate for every domain and data set. Instead, several techniques may need to be integrated into hybrid systems and used cooperatively during a particular data-mining operation. That is, hybrid solutions are crucial for the success of data mining. This paper presents a hybrid framework for identifying patterns from databases or multi-databases. The framework integrates these techniques for mining tasks from an agent point of view. Based on the experiments conducted, putting different KDD techniques together into the agent-based architecture enables them to be used cooperatively when needed. The proposed framework provides a highly flexible and robust data-mining platform and the resulting systems demonstrate emergent behaviors although it does not improve the performance of individual KDD techniques.     

Cross-system user modeling and personalization on the Social Web

In order to adapt functionality to their individual users, systems need information about these users. The Social Web provides opportunities to gather user data from outside the system itself. Aggregated user data may be useful to address cold-start problems as well as sparse user profiles, but this depends on the nature of individual user profiles distributed on the Social Web. For example, does it make sense to re-use Flickr profiles to recommend bookmarks in Delicious? In this article, we study distributed form-based and tag-based user profiles, based on a large dataset aggregated from the Social Web. We analyze the completeness, consistency and replication of form-based profiles, which users explicitly create by filling out forms at Social Web systems such as Twitter, Facebook and LinkedIn. We also investigate tag-based profiles, which result from social tagging activities in systems such as Flickr, Delicious and StumbleUpon: to what extent do tag-based profiles overlap between different systems, what are the benefits of aggregating tag-based profiles. Based on these insights, we developed and evaluated the performance of several cross-system user modeling strategies in the context of recommender systems. The evaluation results show that the proposed methods solve the cold-start problem and improve recommendation quality significantly, even beyond the cold-start.     

Geometric data perturbation for privacy preserving outsourced data mining

Data perturbation is a popular technique in privacy-preserving data mining. A major challenge in data perturbation is to balance privacy protection and data utility, which are normally considered as a pair of conflicting factors. We argue that selectively preserving the task/model specific information in perturbation will help achieve better privacy guarantee and better data utility. One type of such information is the multidimensional geometric information, which is implicitly utilized by many data-mining models. To preserve this information in data perturbation, we propose the Geometric Data Perturbation (GDP) method. In this paper, we describe several aspects of the GDP method. First, we show that several types of well-known data-mining models will deliver a comparable level of model quality over the geometrically perturbed data set as over the original data set. Second, we discuss the intuition behind the GDP method and compare it with other multidimensional perturbation methods such as random projection perturbation. Third, we propose a multi-column privacy evaluation framework for evaluating the effectiveness of geometric data perturbation with respect to different level of attacks. Finally, we use this evaluation framework to study a few attacks to geometrically perturbed data sets. Our experimental study also shows that geometric data perturbation can not only provide satisfactory privacy guarantee but also preserve modeling accuracy well.     

Mining frequent itemsets in large databases: The hierarchical partitioning approach

Although many methods have been proposed to enhance the efficiencies of data mining, little research has been devoted to the issue of scalability – that is, the problem of mining frequent itemsets when the size of the database is very large. This study proposes a methodology, hierarchical partitioning, for mining frequent itemsets in large databases, based on a novel data structure called the Frequent Pattern List (FPL). One of the major features of the FPL is its ability to partition the database, and thus transform the database into a set of sub-databases of manageable sizes. As a result, a divide-and-conquer approach can be developed to perform the desired data-mining tasks. Experimental results show that hierarchical partitioning is capable of mining frequent itemsets and frequent closed itemsets in very large databases.     

Multimedia data mining: state of the art and challenges

Advances in multimedia data acquisition and storage technology have led to the growth of very large multimedia databases. Analyzing this huge amount of multimedia data to discover useful knowledge is a challenging problem. This challenge has opened the opportunity for research in Multimedia Data Mining (MDM). Multimedia data mining can be defined as the process of finding interesting patterns from media data such as audio, video, image and text that are not ordinarily accessible by basic queries and associated results. The motivation for doing MDM is to use the discovered patterns to improve decision making. MDM has therefore attracted significant research efforts in developing methods and tools to organize, manage, search and perform domain specific tasks for data from domains such as surveillance, meetings, broadcast news, sports, archives, movies, medical data, as well as personal and online media collections. This paper presents a survey on the problems and solutions in Multimedia Data Mining, approached from the following angles: feature extraction, transformation and representation techniques, data mining techniques, and current multimedia data mining systems in various application domains. We discuss main aspects of feature extraction, transformation and representation techniques. These aspects are: level of feature extraction, feature fusion, features synchronization, feature correlation discovery and accurate representation of multimedia data. Comparison of MDM techniques with state of the art video processing, audio processing and image processing techniques is also provided. Similarly, we compare MDM techniques with the state of the art data mining techniques involving clustering, classification, sequence pattern mining, association rule mining and visualization. We review current multimedia data mining systems in detail, grouping them according to problem formulations and approaches. The review includes supervised and unsupervised discovery of events and actions from one or more continuous sequences. We also do a detailed analysis to understand what has been achieved and what are the remaining gaps where future research efforts could be focussed. We then conclude this survey with a look at open research directions.     

Tree pattern mining with tree automata constraints

Most work on pattern mining focuses on simple data structures such as itemsets and sequences of itemsets. However, a lot of recent applications dealing with complex data like chemical compounds, protein structures, XML and Web log databases and social networks, require much more sophisticated data structures such as trees and graphs. In these contexts, interesting patterns involve not only frequent object values (labels) appearing in the graphs (or trees) but also frequent specific topologies found in these structures. Recently, several techniques for tree and graph mining have been proposed in the literature. In this paper, we focus on constraint-based tree pattern mining. We propose to use tree automata as a mechanism to specify user constraints over tree patterns. We present the algorithm CoBMiner which allows user constraints specified by a tree automata to be incorporated in the mining process. An extensive set of experiments executed over synthetic and real data (XML documents and Web usage logs) allows us to conclude that incorporating constraints during the mining process is far more effective than filtering the interesting patterns after the mining process.     

寻呼系统中的数据挖掘

寻呼系统中的用户资料库数据具有多维性,如用户类型,开户时间,开户地点,联网方式,速率,BP机类型等。对此类数据的多维分析有助于了解系统负载,资源使用,用户分布,利润等情况。为此,论文运用概念描述的方法对广东省某寻呼台的用户资料库进行了数据挖掘。对寻呼台的领导层制定寻呼台的发展策略可以起到积极的作用。     

ExAnte: a preprocessing method for frequent-pattern mining

Our main research objective is to define a data mining query language, supported by a system that can optimize constraint-based data mining queries. We have invented ExAnte, a simple yet effective preprocessing technique for frequent-pattern mining. ExAnte exploits constraints to dramatically reduce the analyzed data to those containing patterns of interest. This data reduction, in turn, induces a strong reduction of the candidate patterns' search space, thus supporting substantial performance improvements in subsequent mining.     

PARSIMONY: An Infrastructure for Parallel Multidimensional Analysis and Data Mining

Multidimensional analysis and online analytical processing (OLAP) operations require summary information on multidimensional data sets. Most common are aggregate operations along one or more dimensions of numerical data values. Simultaneous calculation of multidimensional aggregates are provided by the Data Cube operator, used to calculate and store summary information on a number of dimensions. This is computed only partially if the number of dimensions is large. Query processing for these applications requires different views of data to gain insight and for effective decision support. Queries may either be answered from a materialized cube in the data cube or calculated on the fly.  The multidimensionality of the underlying problem can be represented both in relational and in multidimensional databases, the latter being a better fit when query performance is the criteria for judgment. Relational databases are scalable in size for OLAP and multidimensional analysis and efforts are on to make their performance acceptable. On the other hand multidimensional databases have proven to provide good performance for such queries, although they are not very scalable. In this article we address (1) scalability in multidimensional systems for OLAP and multidimensional analysis and (2) integration of data mining with the OLAP framework. We describe our system PARSIMONY, parallel and scalable infrastructure for multidimensional online analytical processing, used for both OLAP and data mining. Sparsity of data sets is handled by using chunks to store data either as a dense block using multidimensional arrays or as sparse representation using a bit encoded sparse structure. Chunks provide a multidimensional index structure for efficient dimension oriented data accesses much the same as multidimensional arrays do. Operations within chunks and between chunks are a combination of relational and multidimensional operations depending on whether the chunk is sparse or dense. Further, we develop parallel algorithms for data mining on the multidimensional cube structure for attribute-oriented association rules and decision-tree-based classification. These take advantage of the data organization provided by the multidimensional data model.  Performance results for high dimensional data sets on a distributed memory parallel machine (IBM SP-2) show good speedup and scalability.     

High Performance OLAP and Data Mining on Parallel Computers

On-Line Analytical Processing (OLAP) techniques are increasingly being used in decision support systems to provide analysis of data. Queries posed on such systems are quite complex and require different views of data. Analytical models need to capture the multidimensionality of the underlying data, a task for which multidimensional databases are well suited. Multidimensional OLAP systems store data in multidimensional arrays on which analytical operations are performed. Knowledge discovery and data mining requires complex operations on the underlying data which can be very expensive in terms of computation time. High performance parallel systems can reduce this analysis time. Precomputed aggregate calculations in a Data Cube can provide efficient query processing for OLAP applications. In this article, we present algorithms for construction of data cubes on distributed-memory parallel computers. Data is loaded from a relational database into a multidimensional array. We present two methods, sort-based and hash-based for loading the base cube and compare their performances. Data cubes are used to perform consolidation queries used in roll-up operations using dimension hierarchies. Finally, we show how data cubes are used for data mining using Attribute Focusing techniques. We present results for these on the IBM-SP2 parallel machine. Results show that our algorithms and techniques for OLAP and data mining on parallel systems are scalable to a large number of processors, providing a high performance platform for such applications.     

基于网格的物流资源挖掘模型的研究

分布在因特网上的物流资源具有地理分散和职权自治的特性,资源结构和接口难以统一。该文以网格、Agent和增量挖掘技术为基础,提出了不通过资源整合就能够实现全局信息挖掘的方法。分析了基于Web的物流资源网格系统,将其划分为物流域的集合实施分级管理。提出了新的面向网格的信息挖掘模型并设计了域内动态资源挖掘算法和域间请求式资源挖掘算法。该模型解决了不同物流系统之间的信息挖掘难题,算法中引入的增量挖掘技术提高了域间资源挖掘效率。     

A GA-based Fuzzy Mining Approach to Achieve a Trade-off Between Number of Rules and Suitability of Membership Functions

Data mining is most commonly used in attempts to induce association rules from transaction data. Transactions in real-world applications, however, usually consist of quantitative values. This paper thus proposes a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. We present a GA-based framework for finding membership functions suitable for mining problems and then use the final best set of membership functions to mine fuzzy association rules. The fitness of each chromosome is evaluated by the number of large 1-itemsets generated from part of the previously proposed fuzzy mining algorithm and by the suitability of the membership functions. Experimental results also show the effectiveness of the framework.     

Toward intelligent data warehouse mining: An ontology-integrated approach for multi-dimensional association mining

A data warehouse is an important decision support system with cleaned and integrated data for knowledge discovery and data mining systems. In reality, the data warehouse mining system has provided many applicable solutions in industries, yet there are still many problems causing users extra problems in discovering knowledge or even failing to obtain the real and useful knowledge they need. To improve the overall data warehouse mining process, we present an intelligent data warehouse mining approach incorporated with schema ontology, schema constraint ontology, domain ontology and user preference ontology. The structures of these ontologies are illustrated and how they benefit the mining process is also demonstrated by examples utilizing rule mining. Finally, we present a prototype multidimensional association mining system, which with intelligent assistance through the support of the ontologies, can help users build useful data mining models, prevent ineffective pattern generation, discover concept extended rules, and provide an active knowledge re-discovering mechanism.