High Utility Pattern Mining Research Articles

EHMIN: Efficient approach of list based high-utility pattern mining with negative unit profits

High-utility pattern mining is an important sub-literature in the data mining literature. This literature discusses the discovery of useful pattern information from large databases by considering not only supports of patterns but also profits and quantities of items. This literature has the potential to be applied to various problems in the real world, so many methods for the improvement of the algorithm performance have been studied. Moreover, there have also been attempts to extend the flexibility of this literature. The traditional approaches in this literature considered the positive unit profits of items in a given database only. However, this literature can take extended flexibility into account by considering negative as well as positive unit profits of the items. In this paper, we suggest an efficient approach for mining high-utility patterns with negative unit profits. Moreover, the experimental performance tests, which are performed on various real and synthetic datasets in this paper, show that the proposed algorithm has a better performance than the state-of-the-art methods in this literature in terms of the runtime, memory usage, and scalability.

Analysis of Special Children Education Using Data Mining Approach

Data mining is a method that gives valuable information where we can improve and achieve the goal. Existing data mining techniques are applied in education for analyzing the performance of students, classes, and institutions. This helps the teachers and management to identify where they lag and improvise. Some mining techniques are used to predict and identify special children’s categories. In this paper, data mining techniques are applied for the special children to predict the achievement in special school study for child categories like Mental Retardation (MR), autism, and cerebral palsy using the collected assessment detail. Vocational training is considered an essential aspect for special school children for their future survival. Data mining methods are applied to mine the most achievable and essential training factors as a pattern by apriori rule mining and high utility pattern mining algorithms, based on their assessment achieved from the age 10–14, where Madras developmental programming scale is followed in special schools. We can predict which category children are achieving necessary factors, which helps identify the alternate methods to train the particular activity. Essential factors that cannot achieve at the same level will be trained in the next prevocational level to reach their vocational training. The prediction that we have made will be helpful for the teachers to train the children concerning their achievements.

Frequent high-utility sequential pattern mining algorithm for integrated architecture design of multi-platform mission system

Frequent high-utility sequential pattern mining algorithm for integrated architecture design of multi-platform mission system

An Efficient Approach for Mining Reliable High Utility Patterns

Utility mining is one of the most thriving research topics with a wide range of real-world applications. High utility pattern mining uses a utility function to extract all desired patterns that exceed a minimum utility threshold. However, a significant number of patterns will be generated if this threshold is set too low, which is an inherent limitation of these algorithms. This may cause the mining process to be inefficient as it would be difficult to analyze the patterns found. Furthermore, most of these patterns are unreliable and hard to be employed in making decisions. This paper proposed a novel problem of mining reliable high utility patterns by adapting the concept of reliability to mine a significant type of pattern called reliable high utility patterns. To address this issue, an efficient approach named RUPM (Reliable Utility-based Pattern Mining) is presented. RUPM introduces three novel measurements for estimating the reliability of utility-based patterns and proposes several strategies to efficiently handle reliable patterns with high utility values. Experimental results suggest that up to 99% of the patterns discovered by existing traditional high utility pattern mining algorithms were, in fact, unreliable. In contrast, the average reliability proportion in the resultant patterns obtained from the RUPM approach is at least 47.6% higher. Moreover, the proposed pruning strategies provide a reduction in both the runtime and memory usage.

Mining high utility pattern with negative items in dynamic databases

High utility pattern mining with negative items (HUPMN) has more practical applications because it can process data with negative utility values. But the existing HUPMN algorithms assume that the database is static and it would be very expensive to use these algorithms directly to deal with dynamic databases. To cope with this challenge, an high utility pattern algorithm for mining negative items from an incremental database is proposed for the first time, and an incremental index list structure is designed, which uses index values to quickly access and update the information stored in the list. In addition, A memory reuse strategy is also applied to reduce memory usage. Finally, a HUPMN algorithm based on sliding window was proposed, which can quickly update item information when the window was sliding. Extensive experimental evaluations have been carried out on a variety of data sets, and the results show that the proposed algorithm exhibits excellent performance in terms of running time and memory usage.

Prelarge-Based Utility-Oriented Data Analytics for Transaction Modifications in Internet of Things

The Internet of Things (IoT) environment includes things that exchange information using data generated through sensors. The IoT technology can be executed efficiently by providing the necessary information to things instead of transmitting all the data. There is a need for an algorithm that can extract meaningful information for the things from the data. High utility pattern mining, which can handle the characteristics of real-world databases better than traditional pattern mining methods, has been actively researched. The traditional high utility pattern mining techniques find meaningful patterns from static databases. Therefore, these techniques are not suitable for the dynamically changing databases in the real world. In order to solve this problem, a variety of methods that consider the modifications, deletions, and insertions of transactions are proposed. The pre-large concept, which can be efficiently operated by reducing the rescanning of the original database using two thresholds, was also proposed in order to handle the limitations of the methods that are used for static databases. In the pre-large concept, the large patterns and the pre-large patterns that are discovered are maintained and used for the next transaction change. In this paper, we proposed a new method (PIHUP-MOD) implemented as a tree structure in order to handle the transaction modifications. The method has an efficient structure and techniques to mine high utility patterns in the transaction modifications with the pre-large concept. We conducted the performance evaluation, and the experimental results on the real datasets and the synthetic datasets showed that the proposed approach has a better performance than the state-of-the-art approaches in terms of the runtime, the memory usage, and the scalability.

Efficient mining of cross-level high-utility itemsets in taxonomy quantitative databases

In contrast to frequent itemset mining (FIM) algorithms that focus on identifying itemsets with high occurrence frequency, high-utility itemset mining algorithms can reveal the most profitable sets of items in transaction databases. Several algorithms were proposed to perform the task efficiently. Nevertheless, most of them ignore the item categorizations. This useful information is provided in many real-world transaction databases. Previous works, such as CLH-Miner and ML-HUI Miner were proposed to solve this limitation to discover cross-level and multi-level HUIs. However, the CLH-Miner has a long runtime and high memory usage. To address these drawbacks, this study extends tight upper bounds to propose effective pruning strategies. A novel algorithm named FEACP (Fast and Efficient Algorithm for Cross-level high-utility Pattern mining) is introduced, which adopts the proposed strategies to efficiently identify cross-level HUIs in taxonomy-based databases. It can be seen from a thorough performance evaluation that FEACP can identify useful itemsets of different abstraction levels in transaction databases with high efficiency, that is up to 8 times faster than the state-of-the-art algorithm on the tested sparse databases and up to 177 times on the tested dense databases. FEACP reduces memory usage by up to half over the CLH-Miner algorithm.

Analysis of Top-K Utility Pattern Mining Framework

A new algorithm of utility-list structure has been proposed in this paper which based on the framework of top-k utility mining analysed. There are many techniques which extracts top keyword within search space relates with various algorithms. Like, High Utility Pattern Mining, which is detection of high utility itemsets in a transactional database; provides the fundamental task in database and data mining community, such as quantity, cost, weight and profits concern, to extract remarkable knowledge and efficient database patterns. Different from the support based mining models; the utility oriented mining framework integrates the utility theory to provide more informative and useful patterns. These could not be directly performed on the utility mining techniques with the help of proposed method in this paper would find a top-k search space itemsets. This paper is focused on analysis of many identical high top-k utility patterns mining algorithm, such as mining for Top-K High Utility Itemsets, Solutions to Utility Big Data Analysis, Negative Sequential Patterns Mining, Efficient High Pattern Mining with Tighter Upper Bounds and Tighter Upper Bound including Average-Utility for Mining High Average-Utility Patterns. However there are some issues that need to resolve. These are discussed in this paper and efficient proposed the analysis of the various utility

A Survey of incremental high-utility pattern mining based on storage structure

Traditional association rule mining has been widely studied, but this is not applicable to practical applications that must consider factors such as the unit profit of the item and the purchase quantity. High-utility itemset mining (HUIM) aims to find high-utility patterns by considering the number of items purchased and the unit profit. However, most high-utility itemset mining algorithms are designed for static databases. In real-world applications (such as market analysis and business decisions), databases are usually updated by inserting new data dynamically. Some researchers have proposed algorithms for finding high-utility itemsets in dynamically updated databases. Different from the batch processing algorithms that always process the databases from scratch, the incremental HUIM algorithms update and output high-utility itemsets in an incremental manner, thereby reducing the cost of finding high-utility itemsets. This paper provides the latest research on incremental high-utility itemset mining algorithms, including methods of storing itemsets and utilities based on tree, list, array and hash set storage structures. It also points out several important derivative algorithms and research challenges for incremental high-utility itemset mining.

Efficient Utility Tree-Based Algorithm to Mine High Utility Patterns Having Strong Correlation

High Utility Itemset Mining (HUIM) is one of the most investigated tasks of data mining. It has broad applications in domains such as product recommendation, market basket analysis, e-learning, text mining, bioinformatics, and web click stream analysis. Insights from such pattern analysis provide numerous benefits, including cost cutting, improved competitive advantage, and increased revenue. However, HUIM methods may discover misleading patterns as they do not evaluate the correlation of extracted patterns. As a consequence, a number of algorithms have been proposed to mine correlated HUIs. These algorithms still suffer from the issue of the computational cost in terms of both time and memory consumption. This paper presents an algorithm, named Efficient Correlated High Utility Pattern Mining (ECoHUPM), to efficiently mine the high utility patterns having strong correlation items. A new data structure based on utility tree (UTtree) named CoUTlist is proposed to store sufficient information for mining the desired patterns. Three pruning properties are introduced to reduce the search space and improve the mining performance. Experiments on sparse, very sparse, dense, and very dense datasets indicate that the proposed ECoHUPM algorithm is efficient as compared to the state-of-the-art CoHUIM and CoHUI-Miner algorithms in terms of both time and memory consumption.

Memory-optimized distributed utility mining for big data

In recent days, social media, online services, smartphones, and the Internet of Things (IoT) produces large quantities of data every second. The generated data is structured, unstructured, or semi-structured and available in various formats. Therefore, traditional approaches are not sufficient to handle such kind of data effectively. High utility pattern mining is a famous study area of data analytics that incorporates utility measures to consider user-based constraints such as number of units and benefit, in addition to frequency statistics of datasets. It is also essential to make effective decisions, and their demand is increasing in the last decade. Several techniques have been suggested for utility-based frequent pattern extraction. However, these approaches are limited to data size and operate on standalone systems. We have proposed a parallel approach named distributed memory-optimized utility mining (DMOUM) for high utility-based frequent pattern mining to address this issue. A new pruning technique is implemented that greatly reduces processing time and memory use. The proposed approach can handle huge amounts of data, i.e., big data. DMOUM approach is implemented in cluster-node architecture using Spark. The results are validated on various real-time datasets and found proposed approach has better performance than cutting edge approaches in terms of run-time, memory consumption, and scalability measures. The proposed work will provide solutions to real-time issues such as health care, education, e-commerce, and so on.

A predictive GA-based model for closed high-utility itemset mining

Mining patterns with high utilization (or called high-utility itemset mining, HUIM) is considered a major issue in recent decades especially in the market (e.g., supermarket) engineering since it reveals the profitable information/products for decision-making. Many existing works focused on mining high-utility itemsets from databases that revealed a very large amount of patterns. This process cannot make the precise decision in a short time, e.g., real-time and online decision-making system, since it is not a trivial task to find the appropriate and useful information from a huge amount of the discovered knowledge in a limited time. Mining closed patterns with high utilization (or called closed high-utility pattern mining) is an alternative way to reveal fewer and concise patterns with high utilization in market engineering. However, many past works considered the complete mining progress of all HUIs and they do not consider the correlation between transactions thus when the transactions are not highly relevant, the trained model could not be perfectly used for the predication, which shows inappropriate results in machine learning tasks. In this paper, we consider the clustering models that can divide the transactions into the proper groups based on their correlation, which can be used to make a better accuracy model for prediction. To reduce the mining progress of the CHUIs, a compact GA model is also used in the mining progress, thus in the large-scale situation, a sufficient number of satisfied CHUIs can be discovered in a limited time. Based on the designed framework, the designed model can, not only mine the good enough CHUIs in the limited time for the large-scale environment but also the adopted clustering concept can make a better predictive model in the machine learning process, thus higher accuracy results can be obtained. Experimental results are then evaluated to compare the state-of-the-art CHUI-Miner and CLS-Miner with the developed DcGA model, and the designed DcGA achieves the best runtime performance and satisfied mining results.

A Survey of Utility-Oriented Pattern Mining

The main purpose of data mining and analytics is to find novel, potentially useful patterns that can be utilized in real-world applications to derive beneficial knowledge. For identifying and evaluating the usefulness of different kinds of patterns, many techniques and constraints have been proposed, such as support, confidence, sequence order, and utility parameters (e.g., weight, price, profit, quantity, satisfaction, etc.). In recent years, there has been an increasing demand for utility-oriented pattern mining (UPM, or called utility mining). UPM is a vital task, with numerous high-impact applications, including cross-marketing, e-commerce, finance, medical, and biomedical applications. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods of UPM. First, we introduce an in-depth understanding of UPM, including concepts, examples, and comparisons with related concepts. A taxonomy of the most common and state-of-the-art approaches for mining different kinds of high-utility patterns is presented in detail, including Apriori-based, tree-based, projection-based, vertical-/horizontal-data-format-based, and other hybrid approaches. A comprehensive review of advanced topics of existing high-utility pattern mining techniques is offered, with a discussion of their pros and cons. Finally, we present several well-known open-source software packages for UPM. We conclude our survey with a discussion on open and practical challenges in this field.

Improving Efficiency of High Utility Sequential Pattern Extraction

Text mining used on texts and publications in the biomedical and molecular biology fields is referred to as "biomedical text mining." It is a relatively new area of study at the intersection of computational linguistics, bioinformatics, and natural language processing. Superior usefulness the goal of sequential pattern mining is to identify statistically significant patterns among data instances when the values are presented sequentially. Time series mining is typically regarded as a distinct activity even if it is closely linked since it is typically assumed that the values are discrete. Structured data mining has a unique use known as sequential pattern mining. High utility pattern (HUP) mining is one of the most relevant study areas in data mining nowadays since it is capable of taking into consideration the nonbinary frequency values of items in transactions as well as different profit values for each item. The utilization of previous data structures as well as mining outcomes, yet, enables incremental and interactive data mining to eliminate the need for further calculations when a database is updated or the minimum threshold is modified. The method in this study suggests three innovative tree architectures for effective incremental and interactive HUP mining. The high utility sequential pattern mining issue has formalised key ideas and elements.

RHUPS

Databases that deal with the real world have various characteristics. New data is continuously inserted over time without limiting the length of the database, and a variety of information about the items constituting the database is contained. Recently generated data has a greater influence than the previously generated data. These are called the time-sensitive non-binary stream databases, and they include databases such as web-server click data, market sales data, data from sensor networks, and network traffic measurement. Many high utility pattern mining and stream pattern mining methods have been proposed so far. However, they have a limitation that they are not suitable to analyze these databases, because they find valid patterns by analyzing a database with only some of the features described above. Therefore, knowledge-based software about how to find meaningful information efficiently by analyzing databases with these characteristics is required. In this article, we propose an intelligent information system that calculates the influence of the insertion time of each batch in a large-scale stream database by applying the sliding window model and mines recent high utility patterns without generating candidate patterns. In addition, a novel list-based data structure is suggested for a fast and efficient management of the time-sensitive stream databases. Moreover, our technique is compared with state-of-the-art algorithms through various experiments using real datasets and synthetic datasets. The experimental results show that our approach outperforms the previously proposed methods in terms of runtime, memory usage, and scalability.

Beyond Frequency

Consumer behavior plays a very important role in economics and targeted marketing. However, understanding economic consumer behavior is quite challenging, such as finding credible and reliable information on product profitability. Different from frequent pattern mining, utility-oriented mining integrates utility theory and data mining. Utility mining is a useful tool for understanding economic consumer behavior. Traditional algorithms for mining high-utility patterns (HUPs) applies a single/uniform minimum utility threshold ( minutil ) to obtain the set of HUPs, but in some real-life circumstances, some specific products may bring lower utilities compared with others, but their profit may offer some vital information. If minutil is set high, the patterns with low minutil are missed; if minutil is set low, the number of patterns becomes unmanageable. In this article, an efficient one-phase utility-oriented pattern mining algorithm, called HIMU, is proposed for mining HUPs with varied item-specific minimum utility. A novel tree structure called a multiple item utility set-enumeration tree (MIU-tree) and the global sorted and the conditional downward closure properties are introduced in HIMU. In addition, we extended the compact utility-list structure to keep the necessary information, and thus this one-phase HIMU model greatly reduces the computational costs and memory requirements. Moreover, two pruning strategies are then extended to enhance the performance. We conducted extensive experiments in several synthetic and real-world datasets; the results indicate that the designed one-phase HIMU algorithm can address the “ rare item problem ” and has better performance than the state-of-the-art algorithms in terms of runtime, memory usage, and scalability. Furthermore, the enhanced algorithms outperform the non-optimized HIMU approach.

Mining of High-Utility Patterns in Big IoT-based Databases

When focusing on the general area of data mining, high-utility itemset mining (HUIM) can be defined as an offset of frequent itemset mining (FIM). It is known to emphasize more factors critically, which gives HUIM its intrinsic edge. Due to the flourishing development of the IoT technique, the uncertainty patterns mining is also attractive. Potential high-utility itemset mining (PHUIM) is introduced to reveal valuable patterns in an uncertainty database. Unfortunately, even though the previous methods are all very effective and powerful to mine, the potential high-utility itemsets quickly. These algorithms are not specifically designed for a database with an enormous number of records. In the previous methods, uncertainty transaction datasets would be load in the memory ultimately. Usually, several pre-defined operators would be applied to modify the original dataset to reduce the seeking time for scanning the data. However, it is impracticable to apply the same way in a big-data dataset. In this work, a dataset is assumed to be too big to be loaded directly into memory and be duplicated or modified; then, a MapReduce framework is proposed that can be used to handle these types of situations. One of our main objectives is to attempt to reduce the frequency of dataset scans while still maximizing the parallelization of all processes. Through in-depth experimental results, the proposed Hadoop algorithm is shown to perform strongly to mine all of the potential high-utility itemsets in a big-data dataset and shows excellent performance in a Hadoop computing cluster.

A Survey of Correlated High Utility Pattern Mining

Pattern mining is an unsupervised data mining approach aims to find interesting patterns that can be used to support decision-making. High Utility Pattern Mining (HUPM) aims to extract patterns having high utility or importance which has broad applications in domains such as market basket analysis, product recommendation, bioinformatics, e-learning, text mining, and web click stream analysis. However, it has several limitations on real life scenarios; as a consequence, many extensions of HUPM appeared in the literature such as Correlated High Pattern Mining, Incremental Utility Mining, On-Shelf High Utility Pattern Mining, and Concise Representations of High Utility Patterns. The Correlated High Utility Pattern Mining aims to extract interesting high utility patterns by utilizing both Utility and Correlation measures. Several algorithms have been proposed to mine the correlated high utility patterns. These algorithms differ in the measures used to evaluate the interestingness of the patterns, data structures and pruning properties which they use to improve the mining performance. This paper presents a detailed survey on correlated high utility pattern mining, their methods, measures, data structures and pruning properties.

HUPM-MUO: high utility pattern mining under multiple utility objectives

HUPM-MUO: high utility pattern mining under multiple utility objectives

Fuzzy high-utility pattern mining in parallel and distributed Hadoop framework

Over the past decade, high-utility itemset mining (HUIM) has received widespread attention that can emphasize more critical information than was previously possible using frequent itemset mining (FIM). Unfortunately, HUIM is very similar to FIM since the methodology determines itemsets using a binary model based on a pre-defined minimum utility threshold. Additionally, most previous works only focused on single, small datasets in HUIM, which is not realistic to any real-world scenarios today containing big data environments. In this work, the fuzzy-set theory and a MapReduce framework are both utilized to design a novel high fuzzy utility pattern mining algorithm to resolve the above issues. Fuzzy-set theory is first involved and a new algorithm called efficient high fuzzy utility itemset mining (EFUPM) is designed to discover high fuzzy utility patterns from a single machine. Two upper-bounds are then estimated to allow early pruning of unpromising candidates in the search space. To handle the large-scale of big datasets, a Hadoop-based high fuzzy utility pattern mining (HFUPM) algorithm is then developed to discover high fuzzy utility patterns based on the Hadoop framework. Experimental results clearly show that the proposed algorithms perform strongly to mine the required high fuzzy utility patterns whether in a single machine or a large-scale environment compared to the current state-of-the-art approaches.