Approximate Parallel High Utility Itemset Mining

High utility itemset mining discovers itemsets whose utility is above a given threshold, where utilities measure the importance of itemsets. In high utility itemset mining, memory and time performance limitations cause scalability issues, when the dataset is very large. In this thesis, the problem is addressed by proposing a distributed parallel algorithm, PHUI-Miner, and a sampling strategy, which can be used either separately or simultaneously. PHUI-Miner parallelizes the state-of-the-art high utility itemset mining algorithm HUI-Miner. The sampling strategy investigates the required sample size of a dataset, in order to achieve a given accuracy. We also propose an approach combining sampling with PHUI-Miner, which provides better time performance. In our experiments, we show that PHUI-Miner has high performance and outperforms the state-of-the-art non-parallel algorithm. The sampling strategy achieves accuracies much higher than the guarantee. Extensive experiments are also conducted to compare the time performance of PHUI-Miner with and without sampling

Flexible constrained sampling with guarantees for pattern mining

Pattern sampling has been proposed as a potential solution to the infamous pattern explosion. Instead of enumerating all patterns that satisfy the constraints, individual patterns are sampled proportional to a given quality measure. Several sampling algorithms have been proposed, but each of them has its limitations when it comes to 1) flexibility in terms of quality measures and constraints that can be used, and/or 2) guarantees with respect to sampling accuracy. We therefore present Flexics, the first flexible pattern sampler that supports a broad class of quality measures and constraints, while providing strong guarantees regarding sampling accuracy. To achieve this, we leverage the perspective on pattern mining as a constraint satisfaction problem and build upon the latest advances in sampling solutions in SAT as well as existing pattern mining algorithms. Furthermore, the proposed algorithm is applicable to a variety of pattern languages, which allows us to introduce and tackle the novel task of sampling sets of patterns. We introduce and empirically evaluate two variants of Flexics: 1) a generic variant that addresses the well-known itemset sampling task and the novel pattern set sampling task as well as a wide range of expressive constraints within these tasks, and 2) a specialized variant that exploits existing frequent itemset techniques to achieve substantial speed-ups. Experiments show that Flexics is both accurate and efficient, making it a useful tool for pattern-based data exploration.Comment: Accepted for publication in Data Mining & Knowledge Discovery journal (ECML/PKDD 2017 journal track

Frequent Itemset Mining for Big Data

Traditional data mining tools, developed to extract actionable knowledge from data, demonstrated to be inadequate to process the huge amount of data produced nowadays. Even the most popular algorithms related to Frequent Itemset Mining, an exploratory data analysis technique used to discover frequent items co-occurrences in a transactional dataset, are inefficient with larger and more complex data. As a consequence, many parallel algorithms have been developed, based on modern frameworks able to leverage distributed computation in commodity clusters of machines (e.g., Apache Hadoop, Apache Spark). However, frequent itemset mining parallelization is far from trivial. The search-space exploration, on which all the techniques are based, is not easily partitionable. Hence, distributed frequent itemset mining is a challenging problem and an interesting research topic. In this context, our main contributions consist in an (i) exhaustive theoretical and experimental analysis of the best-in-class approaches, whose outcomes and open issues motivated (ii) the development of a distributed high-dimensional frequent itemset miner. The dissertation introduces also a data mining framework which takes strongly advantage of distributed frequent itemset mining for the extraction of a specific type of itemsets (iii). The theoretical analysis highlights the challenges related to the distribution and the preliminary partitioning of the frequent itemset mining problem (i.e. the search-space exploration) describing the most adopted distribution strategies. The extensive experimental campaign, instead, compares the expectations related to the algorithmic choices against the actual performances of the algorithms. We run more than 300 experiments in order to evaluate and discuss the performances of the algorithms with respect to different real life use cases and data distributions. The outcomes of the review is that no algorithm is universally superior and performances are heavily skewed by the data distribution. Moreover, we were able to identify a concrete lack as regards frequent pattern extraction within high-dimensional use cases. For this reason, we have developed our own distributed high-dimensional frequent itemset miner based on Apache Hadoop. The algorithm splits the search-space exploration into independent sub-tasks. However, since the exploration strongly benefits of a full-knowledge of the problem, we introduced an interleaving synchronization phase. The result is a trade-off between the benefits of a centralized state and the ones related to the additional computational power due to parallelism. The experimental benchmarks, performed on real-life high-dimensional use cases, show the efficiency of the proposed approach in terms of execution time, load balancing and reliability to memory issues. Finally, the dissertation introduces a data mining framework in which distributed itemset mining is a fundamental component of the processing pipeline. The aim of the framework is the extraction of a new type of itemsets, called misleading generalized itemsets

A Survey on Particle Swarm Optimization for Association Rule Mining

Association rule mining (ARM) is one of the core techniques of data mining to discover potentially valuable association relationships from mixed datasets. In the current research, various heuristic algorithms have been introduced into ARM to address the high computation time of traditional ARM. Although a more detailed review of the heuristic algorithms based on ARM is available, this paper differs from the existing reviews in that we expected it to provide a more comprehensive and multi-faceted survey of emerging research, which could provide a reference for researchers in the field to help them understand the state-of-the-art PSO-based ARM algorithms. In this paper, we review the existing research results. Heuristic algorithms for ARM were divided into three main groups, including biologically inspired, physically inspired, and other algorithms. Additionally, different types of ARM and their evaluation metrics are described in this paper, and the current status of the improvement in PSO algorithms is discussed in stages, including swarm initialization, algorithm parameter optimization, optimal particle update, and velocity and position updates. Furthermore, we discuss the applications of PSO-based ARM algorithms and propose further research directions by exploring the existing problems.publishedVersio

Structural advances for pattern discovery in multi-relational databases

With ever-growing storage needs and drift towards very large relational storage settings, multi-relational data mining has become a prominent and pertinent field for discovering unique and interesting relational patterns. As a consequence, a whole suite of multi-relational data mining techniques is being developed. These techniques may either be extensions to the already existing single-table mining techniques or may be developed from scratch. For the traditionalists, single-table mining algorithms can be used to work on multi-relational settings by making inelegant and time consuming joins of all target relations. However, complex relational patterns cannot be expressed in a single-table format and thus, cannot be discovered. This work presents a new multi-relational frequent pattern mining algorithm termed Multi-Relational Frequent Pattern Growth (MRFP Growth). MRFP Growth is capable of mining multiple relations, linked with referential integrity, for frequent patterns that satisfy a user specified support threshold. Empirical results on MRFP Growth performance and its comparison with the state-of-the-art multirelational data mining algorithms like WARMR and Decentralized Apriori are discussed at length. MRFP Growth scores over the latter two techniques in number of patterns generated and speed. The realm of multi-relational clustering is also explored in this thesis. A multi-Relational Item Clustering approach based on Hypergraphs (RICH) is proposed. Experimentally RICH combined with MRFP Growth proves to be a competitive approach for clustering multi-relational data. The performance and iii quality of clusters generated by RICH are compared with other clustering algorithms. Finally, the thesis demonstrates the applied utility of the theoretical implications of the above mentioned algorithms in an application framework for auto-annotation of images in an image database. The system is called CoMMA which stands for Combining Multi-relational Multimedia for Associations