Optimization of High Utility Itemset Mining from Large Transaction Databases on multi core processor

High utility itemset mining is an emerging era that extends frequent itemset mining to identify itemsets in a transaction database with utility values associated with every item above a given threshold. Researchers recently proposed algorithm TWU (Transaction Weighted Utility) has anti-monotone property for pruning the datasets, but it is an overestimate of itemset utility that leads to more search space. In this paper we present an algorithm that takes features of CTU-PROL which is proposed by Researchers. It uses TWU with pattern growth based on a compact utility pattern tree data structure. Our algorithm runs on multi-core processor when the main memory is insufficient to deal with large datasets. An experimental result shows a remarkable speedup for large datasets than the previous algorithms. It can mine large data set more efficiently of both dense and sparse data. DOI: 10.17762/ijritcc2321-8169.150616

Optimized High-Utility Itemsets Mining for Effective Association Mining Paper

Association rule mining is intently used for determining the frequent itemsets of transactional database; however, it is needed to consider the utility of itemsets in market behavioral applications. Apriori or FP-growth methods generate the association rules without utility factor of items. High-utility itemset mining (HUIM) is a well-known method that effectively determines the itemsets based on high-utility value and the resulting itemsets are known as high-utility itemsets. Fastest high-utility mining method (FHM) is an enhanced version of HUIM. FHM reduces the number of join operations during itemsets generation, so it is faster than HUIM. For large datasets, both methods are very expenisve. Proposed method addressed this issue by building pruning based utility co-occurrence structure (PEUCS) for elimatination of low-profit itemsets, thus, obviously it process only optimal number of high-utility itemsets, so it is called as optimal FHM (OFHM). Experimental results show that OFHM takes less computational runtime, therefore it is more efficient when compared to other existing methods for benchmarked large datasets

Extended Apriori for association rule mining: Diminution based utility weightage measuring approach

The field of Association rule mining is a dynamic area for innovation of knowledge through which uncountable procedures have been expounded. Recently, by including significant components viz. value (utility), volume of items (weight) etc, the researchers have enhanced the quality of association rule mining for industry by bringing out the association designs. In this note, a proficient methodology has been put forward based on weight factor and utility for effective digging out of important association rules. At the very beginning, a traditional Apriori algorithm has been utilized that make use of the anti-monotone property which states that if n items are recurring continuously then n-1 items should also recur by which the scores of weightage(W-Gain), utility(U-Gain) and diminution(D-sum), are derived at. Eventually, we derive a subset of important association rules through which EUW-Score is generated. The tentative outcome demonstrates the effectiveness of the methodology in generating high utility association rules that is profitably used for the business improvement

Utilizing Index‑Based Periodic High Utility Mining to Study Frequent Itemsets

The potential employability in diferent applications has garnered more signifcance for Periodic High-Utility Itemset Mining (PHUIM). It is to be noted that the conventional utility mining algorithms focus on an itemset’s utility value rather than that of its periodicity in the transaction. A MEAN periodicity measure is added to the minimum (MIN) and maximum (MAX) periodicity to incorporate the periodicity feature into PHUIM in this proposed work. The MEAN-periodicity measure brings a new dimension to the periodicity factor and is arrived at by dividing itemset’s period value by the total number of transactions in that dataset. Further, an algorithm to mine Index-Based Periodic High Utility Itemset Mining (IBPHUIM) from the database using an indexing approach is also proposed in this paper. The proposed IBPHUIM algorithm employs a projectionbased technique and indexing procedure to increase memory and execution speed efciency. The proposed model avoids redundant database scans by generating sub-databases using an indexing data structure. The proposed IBPHUIM model has experimented with test datasets, and the results drawn show that the proposed IBPHUIM model performs considerably better

AN EFFICIENT ALGORITHM FORMINING HIGH UTILITY ASSOCIATION RULES FROM LATTICE

In business, most of companies focus on growing their profits. Besides considering profit from each product, they also focus on the relationship among products in order to support effective decision making, gain more profits and attract their customers, e.g. shelf arrangement, product displays, or product marketing, etc. Some high utility association rules have been proposed, however, they consume much memory and require long time processing. This paper proposes LHAR (Lattice-based for mining High utility Association Rules) algorithm to mine high utility association rules based on a lattice of high utility itemsets. The LHAR algorithm aims to generates high utility association rules during the process of building lattice of high utility itemsets, and thus it needs less memory and runtim

KHAI THÁC TẬP MỤC LỢI ÍCH CAO CÓ LỢI NHUẬN ÂM TRONG CƠ SỞ DỮ LIỆU PHÂN TÁN DỌC

High Utility Itemset (HUI) mining is an important problem in the data mining literature that considers the utilities for businesses of items (such as profits and margins) that are discovered from transactional databases. There are many algorithms for mining high utility itemsets (HUIs) by pruning candidates based on estimated and transaction-weighted utilization values. These algorithms aim to reduce the search space. In this paper, we propose a method for mining HUIs with negative unit profits from vertically distributed databases. This method does not integrate databases from the relevant local databases to form a centralized database. Experiments show that the run-time of this method is more efficient than that of the centralized database.Tập lợi ích cao (TLIC) là một vấn đề quan trọng trong khai phá dữ liệu, xem xét các lợi ích của các mục (chẳng hạn như lợi nhuận và lãi suất) được khám phá từ cơ sở dữ liệu (CSDL) giao dịch hỗ trợ cho việc kinh doanh của các đơn vị. Bài báo trình bày một phương pháp khai thác tập lợi ích cao có lợi nhuận âm trên CSDL phân tán dọc. Việc khai thác tập lợi ích cao đã được nghiên cứu và công bố rộng rãi trong những năm gần đây. Có nhiều thuật toán khai thác các tập lợi ích cao (TLIC) bằng cách cắt tỉa các ứng cử viên dựa trên các giá trị lợi ích và dựa trên các giá trị sử dụng có trọng số giao dịch. Các thuật toán này đều hướng tới mục đích làm giảm không gian tìm kiếm. Trong bài báo này, chúng tôi đề xuất một phương pháp khai thác tập lợi ích cao có lợi nhuận âm (TLIC-TSA) từ CSDL phân tán dọc. Phương pháp này không tích hợp CSDL từ CSDL cục bộ của các bên tham gia để hình thành CSDL tập trung và chỉ thực hiện việc quét các CSDL mỗi bên tham gia một lần. Các thí nghiệm cho thấy thời gian chạy của phương pháp này hiệu quả hơn so với khai thác trên cơ sở dữ liệu tập trung