The <Emphasis Type="Italic">k</Emphasis>-Nearest Neighbour Join: Turbo Charging the KDD Process

The similarity join has become an important database primitive for supporting similarity searches and data mining. A similarity join combines two sets of complex objects such that the result contains all pairs of similar objects. Two types of the similarity join are well-known, the distance range join, in which the user defines a distance threshold for the join, and the closest pair query or k-distance join, which retrieves the k most similar pairs. In this paper, we propose an important, third similarity join operation called the k-nearest neighbour join, which combines each point of one point set with its k nearest neighbours in the other set. We discover that many standard algorithms of Knowledge Discovery in Databases (KDD) such as k-means and k-medoid clustering, nearest neighbour classification, data cleansing, postprocessing of sampling-based data mining, etc. can be implemented on top of the k-nn join operation to achieve performance improvements without affecting the quality of the result of these algorithms. We propose a new algorithm to compute the k-nearest neighbour join using the multipage index (MuX), a specialised index structure for the similarity join. To reduce both CPU and I/O costs, we develop optimal loading and processing strategies.     

带间隔约束的Top-k对比序列模式挖掘

对比序列模式能够表达序列数据集合间的差异,在商品推荐、用户行为分析和电力供应预测等领域有广泛的应用.已有的对比序列模式挖掘算法需要用户设定正例支持度阈值和负例支持度阈值.在不具备足够先验知识的情况下,用户难以设定恰当的支持度阈值,从而可能错失一些对比显著的模式.为此,提出了带间隔约束的top-k对比序列模式挖掘算法kDSP-Miner(top-k distinguishing sequential patterns with gap constraint miner).kDSP-Miner中用户只需设置期望发现的对比最显著的模式个数,从而避免了直接设置对比支持度阈值.相应地,挖掘算法更容易使用,并且结果更易于解释.同时,为了提高算法执行效率,设计了若干剪枝策略和启发策略.进一步设计了kDSP-Miner的多线程版本,以提高其对高维序列元素情况的处理能力.通过在真实世界数据集上的详实实验,验证了算法的有效性和执行效率.     

Mining Top‐Rank‐k Erasable Itemsets by PID_lists

Mining erasable itemsets are one of new emerging data mining tasks. In this paper, we present a new data representation called a PID_list, which keeps track of the id_nums (identification number) of products that include an itemset. On the basis of the PID_list, we propose a new algorithm called VM for mining top‐rank‐k erasable itemsets efficiently. The VM algorithm can avoid the time‐consuming process of calculating the gain of the candidate itemsets and lots of scans of the databases. Therefore, it can accelerate the task of mining greatly. For evaluating the VM algorithm, we have conducted experiments on six synthetic product databases. Our performance study shows that the VM algorithm is efficient and much faster than the MIKE algorithm, which is the first algorithm for dealing with the problem of mining top‐rank‐k erasable itemsets.     

Efficient mining of maximal frequent itemsets from databases on a cluster of workstations

In this paper, we propose two parallel algorithms for mining maximal frequent itemsets from databases. A frequent itemset is maximal if none of its supersets is frequent. One parallel algorithm is named distributed max-miner (DMM), and it requires very low communication and synchronization overhead in distributed computing systems. DMM has the local mining phase and the global mining phase. During the local mining phase, each node mines the local database to discover the local maximal frequent itemsets, then they form a set of maximal candidate itemsets for the top-down search in the subsequent global mining phase. A new prefix tree data structure is developed to facilitate the storage and counting of the global candidate itemsets of different sizes. This global mining phase using the prefix tree can work with any local mining algorithm. Another parallel algorithm, named parallel max-miner (PMM), is a parallel version of the sequential max-miner algorithm (Proc of ACM SIGMOD Int Conf on Management of Data, 1998, pp 85–93). Most of existing mining algorithms discover the frequent k-itemsets on the kth pass over the databases, and then generate the candidate (k + 1)-itemsets for the next pass. Compared to those level-wise algorithms, PMM looks ahead at each pass and prunes more candidate itemsets by checking the frequencies of their supersets. Both DMM and PMM were implemented on a cluster of workstations, and their performance was evaluated for various cases. They demonstrate very good performance and scalability even when there are large maximal frequent itemsets (i.e., long patterns) in databases.

A practical approximation algorithm for optimal k-anonymity

k-Anonymity is a privacy preserving method for limiting disclosure of private information in data mining. The process of anonymizing a database table typically involves generalizing table entries and, consequently, it incurs loss of relevant information. This motivates the search for anonymization algorithms that achieve the required level of anonymization while incurring a minimal loss of information. The problem of k-anonymization with minimal loss of information is NP-hard. We present a practical approximation algorithm that enables solving the k-anonymization problem with an approximation guarantee of O(ln k). That algorithm improves an algorithm due to Aggarwal et al. (Proceedings of the international conference on database theory (ICDT), 2005) that offers an approximation guarantee of O(k), and generalizes that of Park and Shim (SIGMOD ’07: proceedings of the 2007 ACM SIGMOD international conference on management of data, 2007) that was limited to the case of generalization by suppression. Our algorithm uses techniques that we introduce herein for mining closed frequent generalized records. Our experiments show that the significance of our algorithm is not limited only to the theory of k-anonymization. The proposed algorithm achieves lower information losses than the leading approximation algorithm, as well as the leading heuristic algorithms. A modified version of our algorithm that issues ℓ-diverse k-anonymizations also achieves lower information losses than the corresponding modified versions of the leading algorithms.     

Fast and exact out-of-core and distributed k-means clustering

Clustering has been one of the most widely studied topics in data mining and k-means clustering has been one of the popular clustering algorithms. K-means requires several passes on the entire dataset, which can make it very expensive for large disk-resident datasets. In view of this, a lot of work has been done on various approximate versions of k-means, which require only one or a small number of passes on the entire dataset.In this paper, we present a new algorithm, called fast and exact k-means clustering (FEKM), which typically requires only one or a small number of passes on the entire dataset and provably produces the same cluster centres as reported by the original k-means algorithm. The algorithm uses sampling to create initial cluster centres and then takes one or more passes over the entire dataset to adjust these cluster centres. We provide theoretical analysis to show that the cluster centres thus reported are the same as the ones computed by the original k-means algorithm. Experimental results from a number of real and synthetic datasets show speedup between a factor of 2 and 4.5, as compared with k-means.This paper also describes and evaluates a distributed version of FEKM, which we refer to as DFEKM. This algorithm is suitable for analysing data that is distributed across loosely coupled machines. Unlike the previous work in this area, DFEKM provably produces the same results as the original k-means algorithm. Our experimental results show that DFEKM is clearly better than two other possible options for exact clustering on distributed data, which are down loading all data and running sequential k-means or running parallel k-means on a loosely coupled configuration. Moreover, even in a tightly coupled environment, DFEKM can outperform parallel k-means if there is a significant load imbalance. Ruoming Jin is currently an assistant professor in the Computer Science Department at Kent State University. He received a BE and a ME degree in computer engineering from Beihang University (BUAA), China in 1996 and 1999, respectively. He earned his MS degree in computer science from University of Delaware in 2001, and his Ph.D. degree in computer science from the Ohio State University in 2005. His research interests include data mining, databases, processing of streaming data, bioinformatics, and high performance computing. He has published more than 30 papers in these areas. He is a member of ACM and SIGKDD. Anjan Goswami studied robotics at the Indian Institute of Technology at Kanpur. While working with IBM, he was interested in studying computer science. He then obtained a masters degree from the University of South Florida, where he worked on computer vision problems. He then transferred to the PhD program in computer science at OSU, where he did a Masters thesis on efficient clustering algorithms for massive, distributed and streaming data. On successful completion of this, he decided to join a web-service-provider company to do research in designing and developing high-performance search solutions for very large structured data. Anjan' favourite recreations are studying and predicting technology trends, nature photography, hiking, literature and soccer. Gagan Agrawal is an Associate Professor of Computer Science and Engineering at the Ohio State University. He received his B.Tech degree from Indian Institute of Technology, Kanpur, in 1991, and M.S. and Ph.D degrees from University of Maryland, College Park, in 1994 and 1996, respectively. His research interests include parallel and distributed computing, compilers, data mining, grid computing, and data integration. He has published more than 110 refereed papers in these areas. He is a member of ACM and IEEE Computer Society. He received a National Science Foundation CAREER award in 1998.     

An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis

Clustering is a popular data analysis and data mining technique. A popular technique for clustering is based on k-means such that the data is partitioned into K clusters. However, the k-means algorithm highly depends on the initial state and converges to local optimum solution. This paper presents a new hybrid evolutionary algorithm to solve nonlinear partitional clustering problem. The proposed hybrid evolutionary algorithm is the combination of FAPSO (fuzzy adaptive particle swarm optimization), ACO (ant colony optimization) and k-means algorithms, called FAPSO-ACO–K, which can find better cluster partition. The performance of the proposed algorithm is evaluated through several benchmark data sets. The simulation results show that the performance of the proposed algorithm is better than other algorithms such as PSO, ACO, simulated annealing (SA), combination of PSO and SA (PSO–SA), combination of ACO and SA (ACO–SA), combination of PSO and ACO (PSO–ACO), genetic algorithm (GA), Tabu search (TS), honey bee mating optimization (HBMO) and k-means for partitional clustering problem.     

基于图压缩的k可达查询处理

研究了基于图压缩的k可达查询处理,提出了一种支持k可达查询的图压缩算法k-RPC及无需解压缩的查询处理算法,k-RPC算法在所有基于等价类的支持k-reach查询的图压缩算法中是最优的.由于k-RPC算法是基于严格的等价关系,因此进一步又提出了线性时间的近似图压缩算法k-GRPC.k-GRPC算法允许从原始图中删除部分边,然后使用k-RPC获得更好的压缩比.提出了线性时间的无需解压缩的查询处理算法.真实数据上的实验结果表明,对于稀疏的原始图,两种压缩算法的压缩比分别可以达到45%,对于稠密的原始图,两种压缩算法的压缩比分别可以达到75%和67%;与在原始图上直接进行查询处理相比,两种基于压缩图的查询处理算法效率更好,在稀疏图上的查询效率可以提高2.5倍.     

Mining top-K frequent itemsets from data streams

Frequent pattern mining on data streams is of interest recently. However, it is not easy for users to determine a proper frequency threshold. It is more reasonable to ask users to set a bound on the result size. We study the problem of mining top K frequent itemsets in data streams. We introduce a method based on the Chernoff bound with a guarantee of the output quality and also a bound on the memory usage. We also propose an algorithm based on the Lossy Counting Algorithm. In most of the experiments of the two proposed algorithms, we obtain perfect solutions and the memory space occupied by our algorithms is very small. Besides, we also propose the adapted approach of these two algorithms in order to handle the case when we are interested in mining the data in a sliding window. The experiments show that the results are accurate.

Algorithms for unimodal segmentation with applications to unimodality detection

We study the problem of segmenting a sequence into k pieces so that the resulting segmentation satisfies monotonicity or unimodality constraints. Unimodal functions can be used to model phenomena in which a measured variable first increases to a certain level and then decreases. We combine a well-known unimodal regression algorithm with a simple dynamic-programming approach to obtain an optimal quadratic-time algorithm for the problem of unimodal k-segmentation. In addition, we describe a more efficient greedy-merging heuristic that is experimentally shown to give solutions very close to the optimal. As a concrete application of our algorithms, we describe methods for testing if a sequence behaves unimodally or not. The methods include segmentation error comparisons, permutation testing, and a BIC-based scoring scheme. Our experimental evaluation shows that our algorithms and the proposed unimodality tests give very intuitive results, for both real-valued and binary data. Niina Haiminen received the M.Sc. degree from the University of Helsinki in 2004. She is currently a Graduate Student at the Department of Computer Science of University of Helsinki, and a Researcher at the Basic Research Unit of Helsinki Institute for Information Technology. Her research interests include algorithms, bioinformatics, and data mining. Aristides Gionis received the Ph.D. degree from Stanford University in 2003, and he is currently a Senior Researcher at the Basic Research Unit of Helsinki Institute for Information Technology. His research experience includes summer internship positions at Bell Labs, AT&T Labs, and Microsoft Research. His research areas are data mining, algorithms, and databases. Kari Laasonen received the M.Sc. degree in Theoretical Physics in 1995 from the University of Helsinki. He is currently a Graduate Student in Computer Science at the University of Helsinki and a Researcher at the Basic Research Unit of Helsinki Institute for Information Technology. His research is focused on algorithms and data analysis methods for pervasive computing.     

An efficient approach to mining indirect associations

Discovering association rules is one of the important tasks in data mining. While most of the existing algorithms are developed for efficient mining of frequent patterns, it has been noted recently that some of the infrequent patterns, such as indirect associations, provide useful insight into the data. In this paper, we propose an efficient algorithm, called HI-mine, based on a new data structure, called HI-struct, for mining the complete set of indirect associations between items. Our experimental results show that HI-mine's performance is significantly better than that of the previously developed algorithm for mining indirect associations on both synthetic and real world data sets over practical ranges of support specifications.     

Privacy-preserving data mining: A feature set partitioning approach

In privacy-preserving data mining (PPDM), a widely used method for achieving data mining goals while preserving privacy is based on k-anonymity. This method, which protects subject-specific sensitive data by anonymizing it before it is released for data mining, demands that every tuple in the released table should be indistinguishable from no fewer than k subjects. The most common approach for achieving compliance with k-anonymity is to replace certain values with less specific but semantically consistent values. In this paper we propose a different approach for achieving k-anonymity by partitioning the original dataset into several projections such that each one of them adheres to k-anonymity. Moreover, any attempt to rejoin the projections, results in a table that still complies with k-anonymity. A classifier is trained on each projection and subsequently, an unlabelled instance is classified by combining the classifications of all classifiers.Guided by classification accuracy and k-anonymity constraints, the proposed data mining privacy by decomposition (DMPD) algorithm uses a genetic algorithm to search for optimal feature set partitioning. Ten separate datasets were evaluated with DMPD in order to compare its classification performance with other k-anonymity-based methods. The results suggest that DMPD performs better than existing k-anonymity-based algorithms and there is no necessity for applying domain dependent knowledge. Using multiobjective optimization methods, we also examine the tradeoff between the two conflicting objectives in PPDM: privacy and predictive performance.     

数值型和分类型混合数据的模糊K-Prototypes聚类算法

由于数据库经常同时包含数值型和分类型的属性,因此研究能够处理混合型数据的聚类算法无疑是很重要的.讨论了混合型数据的聚类问题,提出了一种模糊K-prototypes算法.该算法融合了K-means和K-modes对数值型和分类型数据的处理方法,能够处理混合类型的数据.模糊技术体现聚类的边界特征,更适合处理含有噪声和缺失数据的数据库.实验结果显示,模糊算法比相应的确定算法得到的结果准确度高.     

Providing <Emphasis Type="Italic">k</Emphasis>-anonymity in data mining

In this paper we present extended definitions of k-anonymity and use them to prove that a given data mining model does not violate the k-anonymity of the individuals represented in the learning examples. Our extension provides a tool that measures the amount of anonymity retained during data mining. We show that our model can be applied to various data mining problems, such as classification, association rule mining and clustering. We describe two data mining algorithms which exploit our extension to guarantee they will generate only k-anonymous output, and provide experimental results for one of them. Finally, we show that our method contributes new and efficient ways to anonymize data and preserve patterns during anonymization.     

An algorithm for merging meaps

Summary We present an algorithm to merge priority queues organized as heaps. The worst case number of comparisons required to merge two heaps of sizes k and n is O(log(n)*log(k)). The algorithm requires O(k) +log(n)*log (k)) data movements if heaps are implemented using arrays and O(log(n)*log(k)) for a pointer-based implementation. Previous algorithms require either O(n+k) data movements and comparisons, or O(k*log(log(n+k))) comparisons and O(k*log(n+k)) data movements. The algorithm presented in this paper improves on the previous algorithms for the case when k>log(n).This work was done while the authors were at McGill University, Montréal, Canada     

Mining top-k co-occurrence items with sequential pattern

Frequent sequential pattern mining has become one of the most important tasks in data mining. It has many applications, such as sequential analysis, classification, and prediction. How to generate candidates and how to control the combinatorically explosive number of intermediate subsequences are the most difficult problems. Intelligent systems such as recommender systems, expert systems, and business intelligence systems use only a few patterns, namely those that satisfy a number of defined conditions. Challenges include the mining of top-k patterns, top-rank-k patterns, closed patterns, and maximal patterns. In many cases, end users need to find itemsets that occur with a sequential pattern. Therefore, this paper proposes approaches for mining top-k co-occurrence items usually found with a sequential pattern. The Naive Approach Mining (NAM) algorithm discovers top-k co-occurrence items by directly scanning the sequence database to determine the frequency of items. The Vertical Approach Mining (VAM) algorithm is based on vertical database scanning. The Vertical with Index Approach Mining (VIAM) algorithm is based on a vertical database with index scanning. VAM and VIAM use pruning strategies to reduce the search space, thus improving performance. VAM and VIAM are especially effective in mining the co-occurrence items of a long input pattern. The three algorithms were evaluated using real-world databases. The experimental results show that these algorithms perform well, especially VAM and VIAM.     

Efficient mining of association rules by reducing the number of passes over the database

This paper introduces a new algorithm of mining association rules.The algorithm RP counts the itemsets with different sizes in the same pass of scanning over the database by dividing the database into m partitions.The total number of pa sses over the database is only(k 2m-2)/m,where k is the longest size in the itemsets.It is much less than k .     

k-ANMI: A mutual information based clustering algorithm for categorical data

Clustering categorical data is an integral part of data mining and has attracted much attention recently. In this paper, we present k-ANMI, a new efficient algorithm for clustering categorical data. The k-ANMI algorithm works in a way that is similar to the popular k-means algorithm, and the goodness of clustering in each step is evaluated using a mutual information based criterion (namely, average normalized mutual information – ANMI) borrowed from cluster ensemble. This algorithm is easy to implement, requiring multiple hash tables as the only major data structure. Experimental results on real datasets show that k-ANMI algorithm is competitive with those state-of-the-art categorical data clustering algorithms with respect to clustering accuracy.     

Anytime measures for top-k algorithms on exact and fuzzy data sets

Top-k queries on large multi-attribute data sets are fundamental operations in information retrieval and ranking applications. In this article, we initiate research on the anytime behavior of top-k algorithms on exact and fuzzy data. In particular, given specific top-k algorithms (TA and TA-Sorted) we are interested in studying their progress toward identification of the correct result at any point during the algorithms’ execution. We adopt a probabilistic approach where we seek to report at any point of operation of the algorithm the confidence that the top-k result has been identified. Such a functionality can be a valuable asset when one is interested in reducing the runtime cost of top-k computations. We present a thorough experimental evaluation to validate our techniques using both synthetic and real data sets.