Distributed Data Mining Research Articles

PDMiner: 基于云计算的并行分布式数据挖掘工具平台

With the development of information technology and internet, various types of information are increasing explosively. It is still a challenge to discover knowledge from massive information. As a pivotal technology to obtain knowledge, data mining has attracted a large amount of research interest for several decades; however, when dealing with large-scale data, most of previous works are still not as efficient as expected. Therefore, the extension of algorithms to deal with large-scale data and the improvement of executing efficiency have become important issues in data mining. Cloud computing based data mining has become a hot topic recently. In this paper, we develop a parallel and distributed data mining toolkit platform (PDMiner) based on large-scale data processing platform—Hadoop. In PDMiner, we propose to implement various data mining operations, such as data preprocessing, association rule analysis, classification and clustering in a parallel manner. The experimental results show that these parallel algorithms 1) can tackle large-scale data set, up to terabyte; 2) are very high efficiency, since they have good speedup; 3) are easily extended to execute in a cluster of commodity machines, which can make full use of computing resource; 4) are efficient for practical data mining. Additionally, we develop knowledge flow subsystem, which can facilitate the user to define data mining task in PDMiner. Furthermore, we can conveniently integrate new parallel algorithms into PDMiner through flexible interface.

Research on Association Rule Mining Algorithm Based on Distributed Data

Existing data miming algorithms have mostly implemented data mining under centralized environment, but the large-scale database exists in the distributed form. According to the existing problem of the distributed data mining algorithm FDM and its improved algorithms, which exist the problem that the frequent itemsets are lost and network communication cost too much. This paper proposes a association rule mining algorithm based on distributed data (ARADD). The mapping marks the array mechanism is included in the ARADD algorithm, which can not only keep the integrity of the frequent itemsets, but also reduces the cost of network communication. The efficiency of algorithm is proved in the experiment.

A Distributed Association Rules Mining Algorithm

Data mining has attracted a great deal of attention in the information industry in recent years and can be used for applications rangning from business management, production control, and science exploration etc. Most of the existing data mining algorithms are processing in the centralized systems; however, at present large database is usually distributed. Compared with the frequent itemsets lost and high communication traffic in distributed database conventional and improved algorithm FDM, An improved distributed data mining algorithm LTDM based on association roles is proposed. LTDM algorithm introduces the mapping indicated array mechanism to keep the integrity of frequent itemsets and decrease the communication traffic. The experimental results prove the efficiency of the proposed algorithm. The algorithm can be applied to information retrieval and so on in the digital library.

A fast online learning algorithm for distributed mining of BigData

BigData analytics require that distributed mining of numerous data streams is performed in real-time. Unique challenges associated with designing such distributed mining systems are: online adaptation to incoming data characteristics, online processing of large amounts of heterogeneous data, limited data access and communication capabilities between distributed learners, etc. We propose a general framework for distributed data mining and develop an efficient online learning algorithm based on this. Our framework consists of an ensemble learner and multiple local learners, which can only access different parts of the incoming data. By exploiting the correlations of the learning models among local learners, our proposed learning algorithms can optimize the prediction accuracy while requiring significantly less information exchange and computational complexity than existing state-of-the-art learning solutions.

Privacy-preserving restricted boltzmann machine.

With the arrival of the big data era, it is predicted that distributed data mining will lead to an information technology revolution. To motivate different institutes to collaborate with each other, the crucial issue is to eliminate their concerns regarding data privacy. In this paper, we propose a privacy-preserving method for training a restricted boltzmann machine (RBM). The RBM can be got without revealing their private data to each other when using our privacy-preserving method. We provide a correctness and efficiency analysis of our algorithms. The comparative experiment shows that the accuracy is very close to the original RBM model.

A Novel Algorithm for Privacy Preserving Distributed Data Mining

With the development of data mining science and information technology, distributed data mining was considered. Distributed data mining was submitted with different purposes, such as increasing the accuracy of result and using data from multiple sources. With the development of distributed data mining, challenges in this field were introduced soon. The main challenge was the issue of privacy preserving. In the past few years, solutions for this problem have been proposed but each one has had a weakness. Given the importance of knowledge, today’s organizations need to Distributed Data Mining, Our goal in this article is to provide an approach that able to preserve the privacy in distributed mining. Our approach can be implemented on most of the algorithms. Proposed approach has been useed data encryption and some of techniques that used in security.

MASSIVELY DISTRIBUTED CONCEPT DRIFT HANDLING IN LARGE NETWORKS

Massively distributed data mining in large networks such as smart device platforms and peer-to-peer systems is a rapidly developing research area. One important problem here is concept drift, where global data patterns (movement, preferences, activities, etc.) change according to the actual set of participating users, the weather, the time of day, or as a result of events such as accidents or even natural catastrophes. In an important case — when the network is very large but only a few training samples can be obtained at each node locally — no efficient distributed solution is known that could follow concept drift efficiently. This case is characteristic of smart device platforms where each device stores only one local observation or data record related to a learning problem. Here we present two algorithms to handle concept drift. None of the algorithms collects data to a central location, instead models of the data perform random walks in the network, while being improved using an online learning algorithm. The first algorithm achieves adaptivity by maintaining young as well as old models in the network according to a fixed age distribution. The second one measures the performance of models locally, and discards them if they are judged outdated. We demonstrate through a thorough experimental analysis that our algorithms outperform the known competing methods if the number of independent local samples is limited relative to the speed of drift: a typical scenario in our targeted application domains. The two algorithms have different strengths: while the age distribution approach is very simple and efficient, explicit drift detection can be useful in monitoring applications to trigger control action.

A distributed privacy-preserving regularization network committee machine of isolated Peer classifiers for P2P data mining

For distributed data mining in peer-to-peer systems this work describes a completely asynchronous, scalable and privacy-preserving committee machine. Regularization neural networks are used for all the Peer classifiers and the combiner committee in an embedded architecture. The proposed method builds the committee machine using the large amounts of training data distributed over the peers, without moving the data, and with little centralized coordination. At the end of the training phase no Peer will know anything else besides its own local data. This privacy-preserving obligation is a challenging problem for trainable combiners but is crucial in real world applications. Only classifiers are transmitted to other peers to validate their data and send back average accuracy rates in a classical asynchronous peer-to-peer execution cycle. Here the validation set for one classifier becomes the training set of the other and vice versa. From this entirely distributed and privacy-preserving mutual validation a coarse-grained asymmetric mutual validation matrix can be formed to map all Peer members. We demonstrate here that it is possible to exploit this matrix to efficiently train another regularization network as the combiner committee machine.

Knowledge Driven HealthCare Decision Support System using Distributed Data Mining

This paper focuses on a web based decision support system which is based on distributed data mining and designed for the healthcare industry. Innumerable Decision Support Systems are prevailing in the market to cater the needs of the various industries based on various purposes. The importance of any Decision Support System resides on the quality of information it provides compared to other information system which leads to take effective decision making. As most of the records in the healthcare industry are maintained manually, the use of Electronic Health Records is introduced recently which will enable the user to have a clear idea about the service he/ she wants to utilize. Since it takes a lot of time to retrieve the information from the manually maintained records, use of Electronic Health Records plays a major role in the Healthcare industry. The present Decision Support Systems concentrate on the administration part of the Healthcare industry, their features support the physical practitioner, nurses, pharmacists, administrative personnel. The patients who are the main players in the healthcare industry are mainly concentrated for mapping their behaviour, and whether they will come back to the health care service provider again or not. The decision system which is presented here gives much importance for the patients and will be acting as a framework which can be included in the services provided by the hospitals. The feedback of the patients can rank the healthcare service provider and to improve the quality of their service.

A generic and distributed privacy preserving classification method with a worst-case privacy guarantee

It is often necessary for organizations to perform data mining tasks collaboratively without giving up their own data. This necessity has led to the development of privacy preserving distributed data mining. Several protocols exist which deal with data mining methods in a distributed scenario but most of these methods handle a single data mining task. Therefore, if the participating parties are interested in more than one classification methods they will have to go through a series of distributed protocols every time, thus increasing the overhead substantially. A second significant drawback with existing methods is that they are often quite expensive due to the use of encryption operations. In this paper a method has been proposed that addresses both these issues and provides a generic approach to efficient privacy preserving classification analysis in a distributed setting with a worst-case privacy guarantee. The experimental results demonstrate the effectiveness of this method.

Parallel and Distributed Code Clone Detection using Sequential Pattern Mining

This research presents a parallel and distributed data mining approach to code clone detection. It aims to prove the value and importance of deploying parallel and distributed computing for real-time large scale code clone detection. It is implemented this approach in a family of clone detectors, called PD EgyCD (Parallel and Distributed Egypt Clone Detector). In this approach, This research builds on an earlier work of the authors for code clone and plagiarism detection using sequential pattern mining by adding parallelism and distribution to our earlier tool EgyCD. Our approach uses data mining through a tailored Apriori-based algorithm for code clone detection. And it uses parallelization and distribution to achieve excellent performance to scale up to clone detection on very large systems. This approach has been implemented as a database application which leverages the capabilities of modern database tools. Two versions have been developed of this distributed technique. The first one uses client-server technique in which all clients and the server deal with only one database. The second one uses agents where each client acts as a separate agent and has its own database and after working on a sub-problem, it submits its partial solution to the server to finally get the complete solution (set of code clones). Experiments show that agents technique is faster than clientserver one. Distribution enhances performance very much. Speed improvement is a function of the number of clients/agents used. Our conclusion is that data mining, combined with parallel and distributed computing, can efficiently be deployed for code clone detection of very large systems.

Big Data and Distributed Data Mining: An Example of Future Networks

This paper describes the perspective on the analytics of big data generated by sensors and devices on the edge of networks. The paper includes a discussion of the importance of data at the edge of networks where some of ―biggest‖ big data is generated. Also quick overview of emerging technologies, including distributed frameworks such as the Apache Hadoop framework and Apache* Map Reduce.

Distributed RK- Secure Sum Protocol for Privacy Preserving

Secure multi party computation allows several parties to compute some function of their inputs without disclosing the actual input to one another. Secure sum computation is an easily understood example and the component of the various secure multi party computation solutions. Secure sum computation allows parties to compute the sum of their individual inputs without disclosing the inputs to one another. In this paper, we propose a protocol with more security, when a group of the computing parties want to know the data of some other party. I. INTRODUCTION The huge growth of the Internet and its easy access by common man created opportunities for joint computations by multiple parties. All the participating parties for the sake of their mutual benefit want to compute the common function of their inputs but at the same time they are worried about the privacy of their data. This subject of the information security is called secure multi party computation. This subject has two goals; one is the privacy of the individual data inputs and another is the correctness of the result. Mainly two models exist in the literature for the analysis of the secure multi party computation problems. Ideal model of the secure multi party computation uses a Trusted Third Party apart from the participating parties. Parties supply their inputs to the Trusted Third Party. Computation of the function is done by the Trusted Third Party and then the result is sent to all the parties. In this paradigm the trustworthiness of the Trusted Third Party is critically important because if the Trusted Third Party turns corrupt, it can supply the private inputs of one party to others. But it is extensively used model of the secure multi party computation due to its easy implementation and the protocols available which prevent the Trusted Third Party to act maliciously. Real model of the secure multi party computation does not use any Trusted Third Party but the parties themselves agree on some protocol for the computation. The parties behavior in the secure multi party computation is important to consider. An honest party follows the protocol and respects the privacy of other parties. A semi honest party follows the protocol but also tries to learn other information than the result. The corrupt party neither follows the protocol nor respects the privacy of other parties. Different protocols are needed for different secure multi party computation models and the behavior of the party. Solutions are available for secure multi party computation problems using cryptographic techniques, randomization techniques and anonymization methods. The subject of secure multi party computation has been evolved from two party comparison problems (1) to multiparty image template matching problems. Many specific secure multi party computation problems have been defined and analyzed by researchers like Private Information Retrieval, Selective Function Evaluation, Privacy-Preserving Database Query, Privacy-Preserving Geometric Computation, Privacy- Preserving Statistical Analysis, Privacy-Preserving Intrusion Detection and Privacy-Preserving Cooperative Scientific Computation. Based on these general secure multi party computation problems many real life applications have been emerged like Privacy- Preserving Electronic Voting, Privacy-Preserving Bidding and Auctions, Privacy-Preserving Social Network Analysis, Privacy-Preserving Signature and Face Detection, Etc. Secure sum computation problem of secure multi party computation can be defined as: How multiple parties can compute the sum of their input values without disclosing actual values to one another. Secure sum can work as the tool for the secure multi party computation solutions in the privacy preserving distributed data mining problems (2). We proposed a secure sum protocol using random numbers (2). In this paper, we proposed a novel changing neighbors approach Distributed RK secure sum protocol for achieving more security in case a group of the parties collude to know the private data of some other party. 1.1 Distributed Database Partition: - Database is divided into three main portioning horizontal partition, vertical partition and hybrid partition. 1.1.1 Horizontal Partitioning: - Horizontal partitioning divides the whole database into the number of small database according to the row splitting. So that the execution of query will be very fast as well as we will be able to provide more privacy to the portioned database.

A Novel Hidden Markov Model for Credit Card Fraud Detection

Nowadays the customers prefer the most accepted payment mode via credit card for the convenient way of online shopping, paying bills in easiest way. At the same time the fraud transaction risks using credit card is a main problem which should be avoided. There are many data mining techniques available to avoid these risks effectively. In existing research they modelled the sequence of operations in credit card transaction processing using a Hidden Markov Model (HMM) and shown how it can be used for the detection of frauds. To provide better accuracy and to avoid computational complexity in fraud detection in proposed work semi Hidden Markov model (SHMM) algorithm of anomaly detection is presented which computes the distance between the processes monitored by credit card detection system and the perfect normal processes. With this we are implementing another method for fraud detection is that having a key idea is to factorize marginal log-likelihood using a variation distribution over latent variables. An asymptotic approximation, a factorized information criterion (FIC) obtained by applying the Laplace method to each of the factorized components. Our experimental results demonstrate that we can significantly reduce loss due to fraud through distributed data mining of fraud models.

Analysis on Data Mining Model Objected to Internet of Things

Three data mining models suitable to data processing have been proposed according to some technical challenges on the characteristics data for Internet of Things. The first model is the multilayer data mining model, which consists of data collection, data processing, event processing and data mining service. The second model is distributed data mining model that could be used to solve the problem of data stored in different locations. The third model is grid-based data mining model, which could utilize the potentially unlimited amount of data by using grid. At the same time, the paper learns from “decomposition build theory to solve the data mining problem in the internet of things. Finally, the key issues are discussed about model application.

Models of distributed data clustering in peer-to-peer environments

Distributed data mining applies techniques to mine distributed data sources by avoiding the need to first collect the data into a central site. This has a significant appeal when issues of communication cost and privacy put a restriction on traditional centralized methods. Although there has been development on many fronts in distributed data mining, we are still lacking models that abstract the process by showing similarities and contrasts between the different methods. In this paper, we introduce two abstract models for distributed clustering in peer-to-peer environments with different goals. The first is the Locally optimized Distributed Clustering (LDC) model, which aims toward achieving better local clusters at each node, and is facilitated by collaboration through sharing of summarized cluster information. The second is the Globally optimized Distributed Clustering (GDC) model, which aims toward achieving one global clustering solution that is an approximation of centralized clustering. We also report on concrete realizations of the two models that show their benefits, through application in text mining. The LDC model is realized through the Collaborative P2P Clustering algorithm, while the GDC model is realized through the Hierarchically distributed P2P Clustering algorithm. In the former, we show that peer collaboration results in significant increase in local clustering quality. The process utilizes cluster summarization to exchange information between peers. In the latter, we target scalability by structuring the P2P network hierarchically and devise a distributed variant of the k-means algorithm to compute one set of clusters across the hierarchy. We demonstrate through experimental results the effectiveness of both methods and make recommendation on when to use each method.

Knowledge Driven HealthCare Decision Support System using Distributed Data Mining

This paper focuses on a web based decision support system which is based on distributed data mining and designed for the healthcare industry. Innumerable Decision Support Systems are prevailing in the market to cater the needs of the various industries based on various purposes. The importance of any Decision Support System resides on the quality of information it provides compared to other information system which leads to take effective decision making. As most of the records in the healthcare industry are maintained manually, the use of Electronic Health Records is introduced recently which will enable the user to have a clear idea about the service he/she wants to utilize. Since it takes a lot of time to retrieve the information from the manually maintained records, use of Electronic Health Records plays a major role in the Healthcare industry. The present Decision Support Systems concentrate on the administration part of the Healthcare industry; their features support the physical practitioner, nurses, pharmacists, administrative personnel. The patients who are the main players in the healthcare industry are mainly concentrated for mapping their behaviour, and whether they will come back to the health care service provider again or not. The decision system which is presented here gives much importance for the patients and will be acting as a framework which can be included in the services provided by the hospitals. The feedback of the patients can rank the healthcare service provider and to improve the quality of their service.

Efficiency Privacy Preservation Scheme for Distributed Digital Document using Cache-Cache Mechanism

In data mining, Privacy preservation plays a major role to provide an efficient communication for the users. Several methods have been employed for privacy preservation scheme they are Secured Multiparty Computation (SMC), Data Transformation Technique (DTT) and Cryptographic Technique. Most of the recent works concentrated on SMC and DTT as the data mining sources are mainly distributed in nature. However cryptographic techniques need more overhead for preserving privacy in distributed data mining context. All these existing techniques are worked and concentrated on data sharing and not concerned about the privacy preservation between the participants on distributed data mining. To address these issues more sensitively, in this work, present a privacy preservation scheme using cachecache mechanism for distributed digital document which includes text, images, etc.,. In the proposed effective privacy preservation scheme for distributed digital document using cache-cache mechanism [PPS-CCM], digital document sharing is done with cache-cache mechanism to reduce the overhead on increasing document sizes. The cache-cache mechanism maintains a cache inside a cache to preserve the details about the users who shared the secret parts of digital document which has been splitted in a scheme of visual cryptography. An experimental evaluation is done with sample set of digital documents and will be processed with the proposed PPS-CCM to estimate the performance of the proposed work in terms of privacy overhead, intensity of cache-cache for digital document sharing, Participant density with an existing texture overlapping and Fourier filtering schemes for color images in visual cryptography.

Programming knowledge discovery workflows in service‐oriented distributed systems

SUMMARYIn several scientific and business domains, very large data repositories are generated. To find interesting and useful information in those repositories, efficient data mining techniques and knowledge discovery processes must be used. The exploitation of data mining techniques in science helps scientists in hypothesis formation and gives them a support on their scientific practices, whereas in industrial processes, data mining can exploit existing data sources as a real value for companies that can take advantage from the knowledge that can be extracted from their large data sources. Data mining tasks are often composed by multiple stages that may be linked to each other to form various execution flows. Moreover, data mining tasks are often distributed because they involve data and tools located over geographically distributed environments. Therefore, it is fundamental to exploit effective paradigms, such as services and workflows, to model data mining tasks that are both multi‐staged and distributed. This paper discusses data mining services and workflows for analyzing scientific data in high‐performance distributed environments such as Grids and Clouds. We discuss how it is possible to define basic and complex services for supporting distributed data mining tasks in Grids. We also present a workflow formalism and a service‐oriented programming framework, named DIS3GNO, for designing and running distributed knowledge discovery processes in the Knowledge Grid system. DIS3GNO supports all the phases of a knowledge discovery process, including composition, execution, and results visualization. After introducing DIS3GNO, some relevant use cases implemented by it and a performance evaluation of the system are discussed.Copyright © 2012 John Wiley & Sons, Ltd.

Improving diagnostic accuracy using agent-based distributed data mining system

The use of data mining techniques to improve the diagnostic system accuracy is investigated in this paper. The data mining algorithms aim to discover patterns and extract useful knowledge from facts recorded in databases. Generally, the expert systems are constructed for automating diagnostic procedures. The learning component uses the data mining algorithms to extract the expert system rules from the database automatically. Learning algorithms can assist the clinicians in extracting knowledge automatically. As the number and variety of data sources is dramatically increasing, another way to acquire knowledge from databases is to apply various data mining algorithms that extract knowledge from data. As data sets are inherently distributed, the distributed system uses agents to transport the trained classifiers and uses meta learning to combine the knowledge. Commonsense reasoning is also used in association with distributed data mining to obtain better results. Combining human expert knowledge and data mining knowledge improves the performance of the diagnostic system. This work suggests a framework of combining the human knowledge and knowledge gained by better data mining algorithms on a renal and gallstone data set.