Mining Software Repository for Cleaning Bugs Using Data Mining Technique

Despite advances in technological complexity and efforts,software repository maintenance requires reusing the data to reduce the effort and complexity.However,increasing ambiguity,irrelevance,and bugs while extracting similar data during software development generate a large amount of data from those data that reside in repositories.Thus,there is a need for a repository mining technique for relevant and bug-free data prediction.This paper proposes a fault prediction approach using a data-mining technique to find good predictors for high-quality software.To predict errors in mining data,the Apriori algorithm was used to discover association rules by fixing confidence at more than 40%and support at least 30%.The pruning strategy was adopted based on evaluation measures.Next,the rules were extracted from three projects of different domains;the extracted rules were then combined to obtain the most popular rules based on the evaluation measure values.To evaluate the proposed approach,we conducted an experimental study to compare the proposed rules with existing ones using four different industrial projects.The evaluation showed that the results of our proposal are promising.Practitioners and developers can utilize these rules for defect prediction during early software development.

Quantum Algorithm for Mining Frequent Patterns for Association Rule Mining

Maximum frequent pattern generation from a large database of transactions and items for association rule mining is an important research topic in data mining. Association rule mining aims to discover interesting correlations, frequent patterns, associations, or causal structures between items hidden in a large database. By exploiting quantum computing, we propose an efficient quantum search algorithm design to discover the maximum frequent patterns. We modified Grover’s search algorithm so that a subspace of arbitrary symmetric states is used instead of the whole search space. We presented a novel quantum oracle design that employs a quantum counter to count the maximum frequent items and a quantum comparator to check with a minimum support threshold. The proposed derived algorithm increases the rate of the correct solutions since the search is only in a subspace. Furthermore, our algorithm significantly scales and optimizes the required number of qubits in design, which directly reflected positively on the performance. Our proposed design can accommodate more transactions and items and still have a good performance with a small number of qubits.

SWFP-Miner： an efficient algorithm for mining weighted frequent pattern over data streams

Previous weighted frequent pattern （WFP） mining algorithms are not suitable for data streams for they need multiple database scans. In this paper, we present an efficient algorithm SWFP-Miner to mine weighted frequent pattern over data streams. SWFP-Miner is based on sliding window and can discover important frequent pattern from the recent data. A new refined weight definition is proposed to keep the downward closure property, and two pruning strategies are presented to prune the weighted infrequent pattern. Experimental studies are performed to evaluate the effectiveness and efficiency of SWFP-Miner.

Ubiquitous Mining with Interactive Data Mining Agents

Due to the increasing availability and sophistication of data recording techniques, multiple information sources and distributed computing are becoming the important trends of modern information systems. Many applications such as security informatics and social computing require a ubiquitous data analysis platform so that decisions can be made rapidly under distributed and dynamic system environments. Although data mining has now been popularly used to achieve such goals, building a data mining system is, however, a nontrivial task, which may require a complete understanding on numerous data mining techniques as well as solid programming skills. Employing agent techniques for data analysis thus becomes increasingly important, especially for users not familiar with engineering and computational sciences, to implement an effective ubiquitous mining platform. Such data mining agents should, in practice, be intelligent, complete, and compact. In this paper, we present an interactive data mining agent - OIDM （online interactive data mining）, which provides three categories （classification, association analysis, and clustering） of data mining tools, and interacts with the user to facilitate the mining process. The interactive mining is accomplished through interviewing the user about the data mining task to gain efficient and intelligent data mining control. OIDM can help users find appropriate mining algorithms, refine and compare the mining process, and finally achieve the best mining results. Such interactive data mining agent techniques provide alternative solutions to rapidly deploy data mining techniques to broader areas of data intelligence and knowledge informaties.

A non-group parallel frequent pattern mining algorithm based on conditional patterns

Frequent itemset mining serves as the main method of association rule mining.With the limitations in computing space and performance,the association of frequent items in large data mining requires both extensive time and effort,particularly when the datasets become increasingly larger.In the process of associated data mining in a big data environment,the MapReduce programming model is typically used to perform task partitioning and parallel processing,which could improve the execution effciency of the algorithm.However,to ensure that the associated rule is not destroyed during task partitioning and parallel processing,the inner-relationship data must be stored in the computer space.Because inner-relationship data are redundant,storage of these data will significantly increase the space usage in comparison with the original dataset.In this study,we find that the formation of the frequent pattern(FP)mining algorithm depends mainly on the conditional pattern bases.Based on the parallel frequent pattern(PFP)algorithm theory,the grouping model divides frequent items into several groups according to their frequencies.We propose a non-group PFP(NG-PFP)mining algorithm that cancels the grouping model and reduces the data redundancy between sub-tasks.Moreover,we present the NG-PFP algorithm for task partition and parallel processing,and its performance in the Hadoop cluster environment is analyzed and discussed.Experimental results indicate that the non-group model shows obvious improvement in terms of computational effciency and the space utilization rate.

A New Algorithm for Mining Frequent Pattern

Mining frequent pattern in transaction database, time series databases, and many other kinds of databases have been studied popularly in data mining research. Most of the previous studies adopt Apriori like candidate set generation and test approach. However, candidate set generation is very costly. Han J. proposed a novel algorithm FP growth that could generate frequent pattern without candidate set. Based on the analysis of the algorithm FP growth, this paper proposes a concept of equivalent FP tree and proposes an improved algorithm, denoted as FP growth * , which is much faster in speed, and easy to realize. FP growth * adopts a modified structure of FP tree and header table, and only generates a header table in each recursive operation and projects the tree to the original FP tree. The two algorithms get the same frequent pattern set in the same transaction database, but the performance study on computer shows that the speed of the improved algorithm, FP growth * , is at least two times as fast as that of FP growth.

Mining Maximal Frequent Patterns in a Unidirectional FP-tree

Because mining complete set of frequent patterns from dense database could be impractical, an interesting alternative has been proposed recently. Instead of mining the complete set of frequent patterns, the new model only finds out the maximal frequent patterns, which can generate all frequent patterns. FP-growth algorithm is one of the most efficient frequent-pattern mining methods published so far. However, because FP-tree and conditional FP-trees must be two-way traversable, a great deal memory is needed in process of mining. This paper proposes an efficient algorithm Unid_FP-Max for mining maximal frequent patterns based on unidirectional FP-tree. Because of generation method of unidirectional FP-tree and conditional unidirectional FP-trees, the algorithm reduces the space consumption to the fullest extent. With the development of two techniques: single path pruning and header table pruning which can cut down many conditional unidirectional FP-trees generated recursively in mining process, Unid_FP-Max further lowers the expense of time and space.

Fast FP-Growth for association rule mining

In this paper, we propose an efficient algorithm, called FFP-Growth (shortfor fast FP-Growth) , to mine frequent itemsets. Similar to FP-Growth, FFP-Growth searches theFP-tree in the bottom-up order, but need not construct conditional pattern bases and sub-FP-trees,thus, saving a substantial amount of time and space, and the FP-tree created by it is much smallerthan that created by TD-FP-Growth, hence improving efficiency. At the same time, FFP-Growth can beeasily extended for reducing the search space as TD-FP-Growth (M) and TD-FP-Growth (C). Experimentalresults show that the algorithm of this paper is effective and efficient.

An efficient algorithm for mining closed itemsets

This paper presents a new efficient algorithm for mining frequent closed itemsets. It enumerates the closed set of frequent itemsets by using a novel compound frequent itemset tree that facilitates fast growth and efficient pruning of search space. It also employs a hybrid approach that adapts search strategies, representations of projected transaction subsets, and projecting methods to the characteristics of the dataset. Efficient local pruning, global subsumption checking, and fast hashing methods are detailed in this paper. The principle that balances the overheads of search space growth and pruning is also discussed. Extensive experimental evaluations on real world and artificial datasets showed that our algorithm outperforms CHARM by a factor of five and is one to three orders of magnitude more efficient than CLOSET and MAFIA.

Fast Discovering Frequent Patterns for Incremental XML Queries

It is nontrivial to maintain such discovered frequent query patterns in real XML-DBMS because the transaction database of queries may allow frequent updates and such updates may not only invalidate some existing frequent query patterns but also generate some new frequent query patterns. In this paper, two incremental updating algorithms, FUX-QMiner and FUXQMiner, are proposed for efficient maintenance of discovered frequent query patterns and generation the new frequent query patterns when new XMI, queries are added into the database. Experimental results from our implementation show that the proposed algorithms have good performance. Key words XML - frequent query pattern - incremental algorithm - data mining CLC number TP 311 Foudation item: Supported by the Youthful Foundation for Scientific Research of University of Shanghai for Science and TechnologyBiography: PENG Dun-lu (1974-), male, Associate professor, Ph.D, research direction: data mining, Web service and its application, peerto-peer computing.

AN INCREMENTAL UPDATING ALGORITHM FOR MINING ASSOCIATION RULES

In this letter, on the basis of Frequent Pattern(FP) tree, the support function to update FP-tree is introduced, then an Incremental FP (IFP) algorithm for mining association rules is proposed. IFP algorithm considers not only adding new data into the database but also reducing old data from the database. Furthermore, it can predigest five cases to three cases.The algorithm proposed in this letter can avoid generating lots of candidate items, and it is high efficient.

基于滑动窗口含负项的高效用模式挖掘

针对传统高效用模式挖掘均未考虑项的效用值为负,以及对流数据处理的时效性问题,提出一种基于滑动窗口的高效用挖掘算法HUPN_SW。利用一种新定义的滑动窗口正负效用列表PNSWU-List,维护挖掘最近批次高效用模式集所需的所有信息,实现有效的逐批次挖掘,避免重复的数据库扫描,在不产生候选效用模式集的情况下,直接挖掘出高效用模式,使HUPN_SW有效适应于动态流数据。实验结果表明,HUPN_SW算法在运行时间和可扩展性方面有良好表现。

Optimization Management of Industrial Organizations Based on Performance Indicators

This paper proposes an intelligent management system (IMS) to help managers in their delicate and tedious task of exploiting the plethora of data (indicators) contained in management dashboards. This system is based on intelligent agents, ontologies and data mining. It is implemented by PASSI (Process for Agent Societies Specification and Implementation) methods for agent design and implementation, the Methodology for Knowledge Modeling and Hot-Winters for data prediction. Intelligent agents not only track indicators but also store the knowledge of managers within the company. Ontologies are used to manage the representation and presentation aspects of knowledge. Data mining makes it possible to: make the most of all available data;model the industrial process of data selection, exploration and modeling;and transform behaviors into predictive indicators. An instance of the IMS named SYGISS, currently in operation within a large brewery organization, allows us to observe very interesting results: the extraction of indicators is done in less than 5 minutes whereas manual extraction used to take 14 days;the generation of dashboards is instantaneous whereas it used to take 12 hours;the interpretation of indicators is instantaneous whereas it used to take a day;forecasts are possible and are done in less than 5 minutes whereas they did not exist with the old management. These important contributions help to optimize the management of this organization.

一种基于Agent的数据挖掘结果模式推荐模型

结合数据挖掘模式存储和人工智能Agent技术,提出了基于智能个性化Agent的数据挖掘结果模式的推荐模型,该推荐模型能自动对用户兴趣进行分析,并向用户推荐其感兴趣而又值得关注的挖掘结果模式,有一定的实用价值。

基于Agent的知识发现模型的设计

ＫＤＤ（ｔｈｅ Ｋｎｏｗｌｅｄｇｅ Ｄｉｓｃｏｖｅｒｙ ｉｎ Ｄａｔａｂａｓｅ）模型的研究是数据挖掘领域中的一个重要分支，现有的一些模型各有其优势，但又不是完美的，尤其在智能性方面都表现得较差。文章设计了一个基于Ａｇｅｎｔ的智能数据挖掘系统，利用多智能体技术实现了信息的收集、预处理、查询、知识的自动提取、数据挖掘等功能，使整个挖掘过程实现了知识性、智能性，它可以为智能信息系统提供必要的支持。

基于多媒体多智能Agents系统的群体决策支持系统集成化设计研究

分析了Ｄeｓａｎｅｔｉｓ＆Ｇａｌｌｕｐｅ群体决策支持系统（Ｇｒｏｕｐ Ｄｅｃｉｓｉｏｎ Ｓｕｐｐｏｒｔ，简称ＧＤＳＳ）初步设计的通用模式，提出了将多媒体技术、智能Ａｇeｎｔs技术、数据仓库技术Ｉｎｔｅｒｎｅｔ／Ｉｎｔｒａｎｅｔ技术等有机地集成于ＧＤＳＳ的集成化设计方案，并就其关键技术功能结构方案设计进行了研究．

基于Multi-agent的面向中小企业的柔性商务智能平台研究

中小企业资金、人才匮乏,信息化水平低,为了解决中小企业的信息化问题,使其能够应用先进的管理思想,提高其竞争力,构建面向中小企业信息化的基于Multi-agent的柔性商务智能平台。平台实现了信息化的核心内容,只需进行简单的二次开发,即可应用于不同的企业。平台以数据仓库为基础,通过数据挖掘、在线分析处理,能有效提高企业决策的科学性,并有效的解决了企业遗留信息系统及系统的扩展性问题。

基于移动Agent的Web信息智能过滤算法及其实现

讨论了W eb信息过滤的现状,提出了W eb信息过滤应用中移动Agent的结构并设计了基于移动Agent的信息过滤工作模型。该模型由数据挖掘模块、信息过滤模块和相应的数据库模块组成,能够有效地屏蔽非法信息。实际应用表明,该W eb信息过滤模型能够有效地完成对网页信息的过滤和保证内部人员合法地使用网络。

频繁时序模式挖掘方法综述

频繁时序模式挖掘是指从时间序列数据中发现频繁出现的模式或规律的过程,其目的是可以帮助理解时间序列数据中的重要特征,例如周期性、趋势和异常等,有助于预测未来的发展趋势和识别异常情况等。根据近年来的频繁时序模式挖掘方法的相关文献调研,按照关键技术和代表性算法将其分为三类,即基于结构约束的频繁时序模式挖掘方法、基于参数约束的频繁时序模式挖掘方法和基于窗口的频繁时序模式挖掘方法。陈述了频繁时序模式挖掘方法的背景以及各方法的特点;分别介绍了三类挖掘方法的发展以及分类,并从优缺点和性能等方面对各类改进方法进行了详细的对比分析;对频繁时序模式挖掘方法进行归纳和总结,并对频繁时序模式挖掘方法的未来研究方向进行了展望。

基于智能Agent的青岛市高校个性化信息服务系统的构建

基于智能Agent的个性化信息服务是一种以用户为中心的信息服务方式,本文结合青岛市高校信息资源开发和利用的特点阐述了基于智能Agent的青岛市高校个性化信息服务系统的体系结构和设计思想。