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

Exploring Decomposition for Solving Pattern Mining Problems

This article introduces a highly efficient pattern mining technique called Clustering-based Pattern Mining (CBPM). This technique discovers relevant patterns by studying the correlation between transactions in the transaction database based on clustering techniques. The set of transactions is first clustered, such that highly correlated transactions are grouped together. Next, we derive the relevant patterns by applying a pattern mining algorithm to each cluster. We present two different pattern mining algorithms, one applying an approximation-based strategy and another based on an exact strategy. The approximation-based strategy takes into account only the clusters, whereas the exact strategy takes into account both clusters and shared items between clusters. To boost the performance of the CBPM, a GPU-based implementation is investigated. To evaluate the CBPM framework, we perform extensive experiments on several pattern mining problems. The results from the experimental evaluation show that the CBPM provides a reduction in both the runtime and memory usage. Also, CBPM based on the approximate strategy provides good accuracy, demonstrating its effectiveness and feasibility. Our GPU implementation achieves significant speedup of up to 552× on a single GPU using big transaction databases.publishedVersio

On the Complexity of Rule Discovery from Distributed Data

This paper analyses the complexity of rule selection for supervised learning in distributed scenarios. The selection of rules is usually guided by a utility measure such as predictive accuracy or weighted relative accuracy. Other examples are support and confidence, known from association rule mining. A common strategy to tackle rule selection from distributed data is to evaluate rules locally on each dataset. While this works well for homogeneously distributed data, this work proves limitations of this strategy if distributions are allowed to deviate. To identify those subsets for which local and global distributions deviate may be regarded as an interesting learning task of its own, explicitly taking the locality of data into account. This task can be shown to be basically as complex as discovering the globally best rules from local data. Based on the theoretical results some guidelines for algorithm design are derived. --

Scalable Mining of High-Utility Sequential Patterns With Three-Tier MapReduce Model

High-utility sequential pattern mining (HUSPM) is a hot research topic in recent decades since it combines both sequential and utility properties to reveal more information and knowledge rather than the traditional frequent itemset mining or sequential pattern mining. Several works of HUSPM have been presented but most of them are based on main memory to speed up mining performance. However, this assumption is not realistic and not suitable in large-scale environments since in real industry, the size of the collected data is very huge and it is impossible to fit the data into the main memory of a single machine. In this article, we first develop a parallel and distributed three-stage MapReduce model for mining high-utility sequential patterns based on large-scale databases. Two properties are then developed to hold the correctness and completeness of the discovered patterns in the developed framework. In addition, two data structures called sidset and utility-linked list are utilized in the developed framework to accelerate the computation for mining the required patterns. From the results, we can observe that the designed model has good performance in large-scale datasets in terms of runtime, memory, efficiency of the number of distributed nodes, and scalability compared to the serial HUSP-Span approach.acceptedVersio

High Utility Itemsets Identification in Big Data

High utility itemset mining is an important data mining problem which considers profit factors besides quantity from the transactional database. It helps find the most valuable products/items that are difficult to track using only the frequent data mining set. An item that has a high-profit value might be rare in the transactional database despite its tremendous importance. While there are many existing algorithms which generate comparatively large candidate sets while finding high utility itemsets, the major focus is to reduce the computational time significantly with the introduction of pruning strategies. Another aspect of high utility itemset mining is to compute the large dataset. There are very few algorithms that can handle a large dataset to find high utility itemset mining in a parallel (distributed) system. In this thesis, there are two proposed methods: 1) High utility itemset mining using pruning strategies approach (HUI-PR) and 2) Parallel EFIM (EFIM-Par). In the method I, the proposed algorithm constructs the candidate sets in the form of a tree structure, which traverses the itemsets with High Transaction-Weighted Utility (HTWUIs). It uses a pruning strategies to reduce the computational time by refraining the visit to unnecessary nodes of an itemset to reduce the search space. It significantly minimizes the transaction database generated on each node. In the method II, the distributed approach is proposed dividing the search space among different worker nodes to compute high utility itemsets which are aggregated to find the result. The experimental results for both methods show that they significantly improve the execution time for computing the high utility itemsets

Encapsulation of Soft Computing Approaches within Itemset Mining a A Survey

Data Mining discovers patterns and trends by extracting knowledge from large databases. Soft Computing techniques such as fuzzy logic, neural networks, genetic algorithms, rough sets, etc. aims to reveal the tolerance for imprecision and uncertainty for achieving tractability, robustness and low-cost solutions. Fuzzy Logic and Rough sets are suitable for handling different types of uncertainty. Neural networks provide good learning and generalization. Genetic algorithms provide efficient search algorithms for selecting a model, from mixed media data. Data mining refers to information extraction while soft computing is used for information processing. For effective knowledge discovery from large databases, both Soft Computing and Data Mining can be merged. Association rule mining (ARM) and Itemset mining focus on finding most frequent item sets and corresponding association rules, extracting rare itemsets including temporal and fuzzy concepts in discovered patterns. This survey paper explores the usage of soft computing approaches in itemset utility mining

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