Parallel FIM Approach on GPU using OpenCL

In this paper, we describe GPU-Eclat algorithm, a GPU (General Purpose Graphics Processing Unit) enhanced implementation of Frequent Item set Mining (FIM). The frequent itemsets are extracted from a transactional database as it is a essential assignment in data mining field because of its broad applications in mining association rules, time series, correlations etc. The Eclat approach is the typically generate-and-check approach to obtain frequent itemsets from a database with a given minimum support threshold value. OpenCL is a platform independent Open Computing Language for GPU computation. We tested our implementation with an Radeon Dual graphic processor and determine up to 68X speedup as compared with sequential Eclat algorithm on a CPU. In order to map the Eclat algorithm onto the SIMD (Single Instruction Multiple Data) execution model, an array data structure is used to represent the input database and standard dataset is converted to the vertical data layout. In our implementation, we perform a parallelized version of the candidate generation and support counting phases on the GPU. Experimental results show that GPU-Eclat consistently outperforms CPU-based Eclat implementations. Our results reveal the potential for GPGPUs in speeding up data mining algorithms

Enhancing FP-Growth Performance Using Multi-threading based on Comparative Study

The time required for generating frequent patterns plays an important role in mining association rules, especially when there exist a large number of patterns and/or long patterns. Association rule mining has been focused as a major challenge within the field of data mining in research for over a decade. Although tremendous progress has been made, algorithms still need improvements since databases are growing larger and larger. In this research we present a performance comparison between two frequent pattern extraction algorithms implemented in Java, they are the Recursive Elimination (RElim) and FP-Growth, these algorithms are used in finding frequent itemsets in the transaction database. We found that FP-growth outperformed RElim in term of execution time. In this context, multithreading is used to enhance the time efficiency of FP-growth algorithm. The results showed that multithreaded FP-growth is more efficient compared to single threaded FP-growth

Supervised cross-modal factor analysis for multiple modal data classification

In this paper we study the problem of learning from multiple modal data for purpose of document classification. In this problem, each document is composed two different modals of data, i.e., an image and a text. Cross-modal factor analysis (CFA) has been proposed to project the two different modals of data to a shared data space, so that the classification of a image or a text can be performed directly in this space. A disadvantage of CFA is that it has ignored the supervision information. In this paper, we improve CFA by incorporating the supervision information to represent and classify both image and text modals of documents. We project both image and text data to a shared data space by factor analysis, and then train a class label predictor in the shared space to use the class label information. The factor analysis parameter and the predictor parameter are learned jointly by solving one single objective function. With this objective function, we minimize the distance between the projections of image and text of the same document, and the classification error of the projection measured by hinge loss function. The objective function is optimized by an alternate optimization strategy in an iterative algorithm. Experiments in two different multiple modal document data sets show the advantage of the proposed algorithm over other CFA methods

Frequent Itemsets Mining for Big Data: A Comparative Analysis

Itemset mining is a well-known exploratory data mining technique used to discover interesting correlations hidden in a data collection. Since it supports different targeted analyses, it is profitably exploited in a wide range of different domains, ranging from network traffic data to medical records. With the increasing amount of generated data, different scalable algorithms have been developed, exploiting the advantages of distributed computing frameworks, such as Apache Hadoop and Spark. This paper reviews Hadoop- and Spark-based scalable algorithms addressing the frequent itemset mining problem in the Big Data domain through both theoretical and experimental comparative analyses. Since the itemset mining task is computationally expensive, its distribution and parallelization strategies heavily affect memory usage, load balancing, and communication costs. A detailed discussion of the algorithmic choices of the distributed methods for frequent itemset mining is followed by an experimental analysis comparing the performance of state-of-the-art distributed implementations on both synthetic and real datasets. The strengths and weaknesses of the algorithms are thoroughly discussed with respect to the dataset features (e.g., data distribution, average transaction length, number of records), and specific parameter settings. Finally, based on theoretical and experimental analyses, open research directions for the parallelization of the itemset mining problem are presented