An incremental algorithm for frequent itemset mining on spark

Individualized boosting learning for classification

Frequent itemsets (FIs) mining from uncertain database is a very popular research area nowadays. Many algorithms have been proposed to mine FI from uncertain database. But in typical FI mining process, all the FIs have to be mined individually, which needs a huge memory. Four trees are proposed in this paper which are: (i) maximal frequent itemset from uncertain database (MFU) tree which contains only the maximal frequent itemsets generated from uncertain database, (ii) closed frequent itemset from uncertain database (CFU) tree which contains only closed frequent itemsets generated from uncertain database, (iii) maximal frequent itemset from uncertain data stream (MFUS) tree which contains maximal frequent itemsets generated from uncertain data stream and (iv) closed frequent itemset from uncertain data stream (CFUS) tree which contains closed frequent itemsets generated from uncertain data stream. Experimental results are also presented which show that maximal and closed frequent itemsets mining requires less time and memory than typical frequent itemsets mining.

Benchmarks have shown to be an important tool to advance science in the fields of information analysis and retrieval. Problems of running benchmarks include obtaining large amounts of data, annotating it and then distributing it to the participants of a benchmark. Distribution of the data to participants is currently mostly done via data download that can take hours for large data sets and in countries with slow Internet connections even days. Sending physical hard disks was also used for distributing very large scale data sets (for example by TRECvid) but also this becomes infeasible if the data sets reach sizes of 5–10 TB. With cloud computing it is possible to make very large data sets available in a central place with limited costs. Instead of distributing the data to the participants, the participants can compute their algorithms on virtual machines of the cloud providers. This text presents reflections and ideas of a concrete project on using cloud–based benchmarking paradigms for medical image analysis and retrieval. It is planned to run two evaluation campaigns in 2013 and 2014 using the proposed technology.

In conventional distributed machine learning methods, distributed support vector machines (SVM) algorithms are trained over pre-configured intranet/internet environments to find out an optimal classifier. These methods are very complicated and costly for large datasets. Hence, we propose a method that is referred as the Cloud SVM training mechanism (CloudSVM) in a cloud computing environment with MapReduce technique for distributed machine learning applications. Accordingly, (i) SVM algorithm is trained in distributed cloud storage servers that work concurrently; (ii) merge all support vectors in every trained cloud node; and (iii) iterate these two steps until the SVM converges to the optimal classifier function. Single computer is incapable to train SVM algorithm with large scale data sets. The results of this study are important for training of large scale data sets for machine learning applications. We provided that iterative training of splitted data set in cloud computing environment using SVM will converge to a global optimal classifier in finite iteration size.

Frequent itemset mining is a popular and important first step in the analysis of data arising in a broad range of applications. The traditional “exact” model for frequent itemsets requires that every item occur in each supporting transaction. However, real data is typically subject to noise and measurement error. To date, the effect of noise on exact frequent pattern mining algorithms have been addressed primarily through simulation studies, and there has been limited attention to the development of noise tolerant algorithms. In this paper we propose a noise tolerant itemset model, which we call approximate frequent itemsets (AFI). Like frequent itemsets, the AFI model requires that an itemset has a minimum number of supporting transactions. However, the AFI model tolerates a controlled fraction of errors in each item and each supporting transaction. Motivating this model are theoretical results (and a supporting simulation study presented here) which state that, in the presence of even low levels of noise, large frequent itemsets are broken into fragments of logarithmic size; thus the itemsets cannot be recovered by a routine application of frequent itemset mining. By contrast, we provide theoretical results showing that the AFI criterion is well suited to recovery of block structures subject to noise. We developed and implemented an algorithm to mine AFIs that generalizes the level-wise enumeration of frequent itemsets by allowing noise. We propose the noise-tolerant support threshold, a relaxed version of support, which varies with the length of the itemset and the noise threshold. We exhibit an Apriori property that permits the pruning of an itemset if any of its sub-itemset is not sufficiently supported. Several experiments presented demonstrate that the AFI algorithm enables better recoverability of frequent patterns under noisy conditions than existing frequent itemset mining approaches. Noise-tolerant support pruning also renders an order of magnitude performance gain over existing methods.

Advances in mass spectrometry-based proteomics have yielded a substantial mapping of the tyrosine phosphoproteome and thus provided an important step toward a systematic analysis of intracellular signaling networks in higher eukaryotes. In this study we decomposed an uncharacterized proteomics data set of 481 unique phosphotyrosine (Tyr(P)) peptides by sequence similarity to known ligands of the Src homology 2 (SH2) and the phosphotyrosine binding (PTB) domains. From 20 clusters we extracted 16 known and four new interaction motifs. Using quantitative mass spectrometry we pulled down Tyr(P)-specific binding partners for peptides corresponding to the extracted motifs. We confirmed numerous previously known interaction motifs and found 15 new interactions mediated by phosphosites not previously known to bind SH2 or PTB. Remarkably, a novel hydrophobic N-terminal motif ((L/V/I)(L/V/I)pY) was identified and validated as a binding motif for the SH2 domain-containing inositol phosphatase SHIP2. Our decomposition of the in vivo Tyr(P) proteome furthermore suggests that two-thirds of the Tyr(P) sites mediate interaction, whereas the remaining third govern processes such as enzyme activation and nucleic acid binding.

This paper addresses the problem of association rules mining with large scale data sets using bees behaviors. The bees swarm optimization method have been successfully running on small and medium data size. Nevertheless, when dealing with large benchmark, it is bluntly blocked. Additionally, graphic processor units are massively threaded providing highly intensive computing and very usable by the optimization research community. The parallelization of such method on GPU architecture can be deal large data sets as the case of WebDocs in real time. In this paper, the evaluation process of the solutions is parallelized. Experimental results reveal that the suggested method outperforms the sequential version at the order of ×70 in most data sets, furthermore, the WebDocs benchmark is handled with less than forty hours.Keywordsbees swarm optimizationassociation rule miningparallel algorithmsGPU architecture

Efficient and accurate personalized product recommendations through frequent item set mining fusion algorithm

Advanced medical imaging technologies have enabled biologists and other researchers in biomedicine, biochemistry and bio-informatics to gain better insight in complex, large-scale data sets. These datasets, which occupy large amounts of space, can no longer be stored on local hard drives. San Diego Supercomputer Center (SDSC) maintains a large data repository, called High Performance Storage System (HPSS), where large-scale biomedical data sets can be stored. These data sets must be transmitted over an open or closed network (Internet or Intranet) within a reasonable amount of time to make them accessible in an interactive fashion to the researchers all over the world. Our approach deals with extracting, compressing and transmitting these data sets using the Haar wavelets, over a low- to medium-bandwidth network. These compressed data sets are then transformed and reconstructed into a 3-D volume on the client side using texture mapping in Java3D. These data sets are handled using the Scalable Visualization Toolkits provided by the NPACI (National Partnership for Advanced Computational Infrastructure). Sub-volumes of the data sets are extracted to provide a detailed view of a particular region of interest (ROI). This application is being ported to C++ platform to obtain higher rendering speed and better performance but lacks platform independency.

Frequent itemsets mining is one of the interesting applications of data mining. Recently data mining has got a great deal of attention due to the explosive growth in data and the economic and scientific need for turning such data into useful information. However, the traditional frequent itemsets mining algorithms have become inefficient to work with large datasets effectively on a single machine due to computational power and memory limits. Current methods prefer to control the execution time and output by using higher minimum support thresholds, which lead to less candidates and frequent itemsets. In this paper, an improved-version of Apriori like HFDM-EB algorithm that can deal with lower minimum support thresholds is proposed for mining frequent itemsets over big transactional data on Hadoop framework and by utilizing compressed bitmaps. The experimental results show that the improved algorithm is efficient and scalable for mining frequent itemsets in big data.

Data mining has made a significant impact on business and knowledge management in recent years. Data mining also known as KDD is a powerful new technology having great potential to analyze useful information stored in large data bases. Association rule mining is an active research area, used to find association and/or correlation among frequent item sets. Association rule mining discover frequent item sets with out considering total cost, quantity and number of items in a transaction. Utility based frequent item set mining is a new research area, which provide importance to sale quantity and price among items in a transaction. In this paper, we have proposed a novel approach for utility frequent item set mining. Our method mines novel frequent item sets by giving importance to items quantity, significance weightage, utility and user defined support. Our approach can be used to provide valuable recommendation to the enterprise to improve business utility.

Frequent itemset mining is an important problem in the data mining area. Extensive efforts have been devoted to developing efficient algorithms for mining frequent itemsets. However, not much attention is paid on managing the large collection of frequent itemsets produced by these algorithms for subsequent analysis and for user exploration. In this paper, we study three structures for indexing and querying frequent itemsets: inverted files, signature files and CFP-tree. The first two structures have been widely used for indexing general set-valued data. We make some modifications to make them more suitable for indexing frequent itemsets. The CFP-tree structure is specially designed for storing frequent itemsets. We add a pruning technique based on length-2 frequent itemsets to make it more efficient for processing superset queries. We study the performance of the three structures in supporting five types of containment queries: exact match, subset/superset search and immediate subset/superset search. Our results show that no structure can outperform other structures for all the five types of queries on all the datasets. CFP-tree shows better overall performance than the other two structures.

Frequent itemset mining over dynamic data is an important problem in the context of knowledge discovery and data mining. Various data stream models are being used for mining frequent itemsets. In a data stream model the data arrive at high speed such that the algorithms used for mining data streams must process them in strict constraint of time and space. Due to emphasis over recent data and its bounded memory requirement, sliding window model is a widely used model for mining frequent itemset over data stream. In this paper we proposed an algorithm named Variable-Moment for mining both frequent and closed frequent itemset over data stream. The algorithm is appropriate for noticing latest or new changes in the set of frequent itemset by making its window size variable, which is determined dynamically based on the extent of concept drift occurring within the arriving data stream. The size of window expands when there is no concept drift in the arriving data stream and size shrinks when there is a concept change. The relative support instead of absolute support is being used for making the concept of variable window effective. The algorithm uses an in-memory data structure to store frequent itemsets. Data structure gets updated whenever a batch of transaction is added or deleted from the sliding window to output exact frequent itemsets. Extensive experiments on both real and synthetic data show that our algorithm excellently spots the concept changes and adapts itself to the new concept along data stream by adjusting window size.

The step of mining frequent itemsets in database is the essential step and most expensive in the process of mining association rules in data mining task, many algorithms of mining frequent itemsets have been proposed to improve the performance of Apriori Algorithm. In this paper, we have introduced an optimization in the phase of generation pruning of candidates by a new strategy for the calculation of frequent itemsets based on approximate values of supports exact the itemsets. We have evaluated our algorithm AprioriMin against three popular frequent itemsets mining algorithms — Apriori and FP-growth, Close using two data sets with a variety of minimum support.

Interests in skyline query processing have significantly increased since it can be used in many applications, such as multi-criteria decision making, data mining, and user preference queries. In this paper, we propose continuous skyline queries over dynamic data set. Since the dynamic data set constantly changes as time passes, continuous skyline queries processing over dynamic data set becomes ever more complicated compared with static data set. We propose a multiple layer grids scheme for efficiently processing continuous skyline queries over skewed data set. The proposed scheme divides the work space into multiple layer grids and creates the skyline influence regions of each layer grid based on the existing data set. The continuous skyline queries are handled only when the dynamic data points update within the skyline influence regions of each layer grid. Since the multiple layer grids are divided dynamically, it is feasible even for the highly skewed data set. Experiments based on various synthetic data sets and real data sets show that our proposed scheme outperforms the existing schemes.