Top-k High Utility Research Articles

TKU-BChOA: an accurate meta-heuristic method to mine Top-k high utility itemsets

TKU-BChOA: an accurate meta-heuristic method to mine Top-k high utility itemsets

Mining Top-k High On-shelf Utility Itemsets Using Novel Threshold Raising Strategies

High utility itemsets (HUIs) mining is an emerging area of data mining which discovers sets of items generating a high profit from transactional datasets. In recent years, several algorithms have been proposed for this task. However, most of them do not consider the on-shelf time period of items and negative utility of items. High on-shelf utility itemset (HOUIs) mining is more difficult than traditional HUIs mining because it deals with on-shelf-based time period and negative utility of items. Moreover, most algorithms need minimum utility threshold ( min_util ) to find rules. However, specifying the appropriate min_util threshold is a difficult problem for users. A smaller min_util threshold may generate too many rules and a higher one may generate a few rules, which can degrade performance. To address these issues, a novel top-k HOUIs mining algorithm named TKOS ( T op- K high O n- S helf utility itemsets miner) is proposed which considers on-shelf time period and negative utility. TKOS presents a novel branch and bound-based strategy to raise the internal min_util threshold efficiently. It also presents two pruning strategies to speed up the mining process. In order to reduce the dataset scanning cost, we utilize transaction merging and dataset projection techniques. Extensive experiments have been conducted on real and synthetic datasets having various characteristics. Experimental results show that the proposed algorithm outperforms the state-of-the-art algorithms. The proposed algorithm is up to 42 times faster and uses up-to 19 times less memory compared to the state-of-the-art KOSHU. Moreover, the proposed algorithm has excellent scalability in terms of time periods and the number of transactions.

Incremental Top-k High Utility Pattern Mining and Analyzing Over the Entire Accumulated Dynamic Database

Incremental Top-k High Utility Pattern Mining and Analyzing Over the Entire Accumulated Dynamic Database

An Approach for Mining Top-k High Utility Item Sets (HUI)

Itemsets have been extracted by utilising high utility item (HUI) mining, which provides more benefits to the consumer. This could be one of the significant domains in data mining and be resourceful for several real-time implementations. Even though modern HUI mining algorithms may identify item sets that meet the minimum utility threshold, However, fixing the minimum threshold utility value has not been a simple task, and often it is intricate for the consumers when we keep the minimum utility value low. It might generate a massive amount of itemsets, and when the value is at its maximum, it might provide a smaller amount of itemsets. To avoid these issues, top-k HUI mining, where k represents the number of HUIs to be identified, has been proposed. Further, in this manuscript, the authors projected an algorithm called the top-k exact utility (TKEU) algorithm, which works without computing and comparing transaction weighted utilisation (TWU) values and deliberates the individual utility item values for deriving the top-k HUI. The datasets are pre-processed by the proposed algorithm to lessen the system memory space and to provide optimal outcomes for condensed datasets.

Efficient mining of top-k high utility itemsets through genetic algorithms

Mining high utility itemsets is an emerging and very active research area in data mining. The goal is to mine all itemsets with a utility value, in terms of importance to the user, no less than a predefined threshold value. Setting an appropriate threshold value is not trivial, requiring not only multiple trials but also the know-how in the application field. The advantage of algorithms for mining top-k high utility itemsets is they do not require such a utility threshold, but they suffer from very long runtimes and large memory requirements when large input data is considered. We propose a new genetic algorithm for mining top-k high utility itemsets, named TKHUIM-GA (Top-K High Utility Itemset Mining through Genetic Algorithms). It guides the search process by considering the utility of each item to produce initial solutions and to combine solutions accordingly, reducing the runtime and memory consumption as a result. A highly efficient data representation is utilized to reduce memory usage and runtime. A key advantage of TKHUIM-GA is that it works on positive, negative, integer and real unit utility values unlike existing approaches. Experiments on popular benchmark datasets demonstrate the high performance of the proposal regarding the state-of-the-art algorithms.

FTKHUIM: A Fast and Efficient Method for Mining Top-K High-Utility Itemsets

FTKHUIM: A Fast and Efficient Method for Mining Top-K High-Utility Itemsets

TKN: An efficient approach for discovering top-k high utility itemsets with positive or negative profits

Top-k high utility itemsets (HUIs) mining permits discovering the required number of patterns - k, without having an optimal minimum utility threshold (i.e., minimum profit). Multiple top-k HUIs mining algorithms have been introduced with interesting results. However, these algorithms focus mainly on mining patterns from positive profit datasets, while few preliminary studies can handle datasets with negative profits. Moreover, conventional top-k HUI mining algorithms, that are meant for exploring positive profit datasets, perform poorly when mining top-k HUIs on highly dense and large datasets. In this paper, we propose TKN (efficiently mining Top-K HUIs with positive or Negative profits) which employs generalized and adaptive techniques to mine both positive and negative profit datasets effectively. The proposed approach adopts transactional projection and merging mechanisms to decrease the dataset traversing cost. Furthermore, several pruning and threshold elevating ideas are utilized to significantly narrow the exploration space. To highlight the reliability of the devised TKN, a series of extensive comparisons were conducted using two versions of six real datasets. The obtained results reveal that TKN is clearly superior in finding the required number of patterns, whether on positive or negative profit datasets, compared to the current cutting-edge competitors.

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

ETKDS: An efficient algorithm of Top-K high utility itemsets mining over data streams under sliding window model

The researcher proposed the concept of Top-K high-utility itemsets mining over data streams. Users directly specify the number K of high-utility itemsets they wish to obtain for mining with no need to set a minimum utility threshold. There exist some problems in current Top-K high-utility itemsets mining algorithms over data streams including the complex construction process of the storage structure, the inefficiency of threshold raising strategies and utility pruning strategies, and large scale of the search space, etc., which still can not meet the requirement of real-time processing over data streams with limited time and memory constraints. To solve this problem, this paper proposes an efficient algorithm based on dataset projection for mining Top-K high-utility itemsets from a data stream. A data structure CIUDataListSW is also proposed, which stores the position of the item in the transaction to effectively obtain the initial projected dataset of the item. In order to improve the projection efficiency, this paper innovates a new reorganization technology for projected transactions in common batches to maintain the sort order of transactions in the process of dataset projection. Dual pruning strategy and transaction merging mechanism are also used to further reduce search space and dataset scanning costs. In addition, based on the proposed CUDHSW structure, an efficient threshold raising strategy CUD is used, and a new threshold raising strategy CUDCB is designed to further shorten the mining time. Experimental results show that the algorithm has great advantages in running time and memory consumption, and it is especially suitable for the mining of high-utility itemsets of dense datasets.

TKUS: Mining top-k high utility sequential patterns

High-utility sequential pattern mining (HUSPM) has recently emerged as a focus of intense research interest. The main task of HUSPM is to find all subsequences, within a quantitative sequential database, that have high utility with respect to a user-defined minimum utility threshold. However, it is difficult to specify the minimum utility threshold, especially when database features, which are invisible in most cases, are not understood. To handle this problem, top- k HUSPM was proposed. Up to now, only very preliminary work has been conducted to capture top- k HUSPs, and existing strategies require improvement in terms of running time, memory consumption, unpromising candidate filtering, and scalability. Moreover, no systematic problem statement has been defined. In this paper, we formulate the problem of top- k HUSPM and propose a novel algorithm called TKUS. To improve efficiency, TKUS adopts a projection and local search mechanism and employs several schemes, including the Sequence Utility Raising, Terminate Descendants Early, and Eliminate Unpromising Items strategies, which allow it to greatly reduce the search space. Finally, experimental results demonstrate that TKUS can achieve sufficiently good top- k HUSPM performance compared to state-of-the-art algorithm TKHUS-Span.

Efficient top-k high utility itemset mining on massive data

In practical applications, top-k high utility itemset mining (top-k HUIM) is an interesting operation to find the k itemsets with the highest utilities. It is analyzed that, the existing algorithms only can deal with the small and medium-sized data, and their performance degrades significantly on massive data. This paper presents a novel top-k HUIM algorithm PTM which can mine top-k high utility itemsets on massive data efficiently. PTM maintains the transaction database as a set of prefix-based partitions, each of which can fit in the memory and keeps the transactions with the same prefix item. The utility of an itemset can be computed by the transactions in one particular partition. PTM processes the prefix-based partitions in the order of average transaction utility to raise utility threshold faster. By the concise assistant data structures, PTM can skip majority of the partitions directly. For the partitions to be processed, PTM utilizes depth-first search on the set enumeration tree of the promising items to find the required results. The full-suffix-utility-based subtree pruning rule is devised to reduce the exploration space of set enumeration tree effectively. The extensive experimental results show that PTM can discover the top-k high utility itemsets on massive data efficiently.

HUPM-MUO: High Utility Pattern Mining under Multiple Utility Objectives

Mining the pattern of interesting items plays a significant role in data analysis and decision-making strategies of real-time applications. Often the term 'interest' in pattern discovery denotes the frequency of the pattern. In recent research, the domain of data mining is considering the utility of the item instead of frequency, which indicates often profit. This manuscript argues that neither utility nor frequency of the itemset alone influence the target objective. Moreover, the profit is not only the utility factor of the itemset, apart from profit, the objectives like storage, saleability and other domain specific requirements can also be the utility factors. In regard to this argument, the manuscript endeavoured to define a novel model that discovers the top-k high utility patterns under multiple utility objectives (HUPM-MUO). The experimental study was carried on various datasets, which portray the performance advantage of the proposed model over the other contemporary models.

An Innovative Procedure for Efficient Mining of Closed Top-K High Utility Itemsets

High utilization itemset (HUI) mining is the fastest growing ground in association finding between the items. It is a process of finding the itemsets with higher utility values which participates in profitable decision making. The generated HUIs reflect the frequency, importance, profit or the utility of the items present in the database. Proper minimum threshold setting is very difficult for the end users without the knowledge of the data present in the database. Minimum user threshold extracts more number of candidate sets . Higher user threshold gives less number of candidate sets and very few high utility itemsets . In both the cases, the process is inefficient. Some algorithms produce more number of candidate itemsets as HUIs. The set of HUIs may degrade the performance of the candidate set mining by increasing the storage and time when the database has very large number of transactions. The number of candidate itemsets involved in the generation of HUIs may also slow down the entire process. The proposed novel strategy for tapping top-k closed high utility itemsets out of the set of candidate sets addresses these issues . The user defined integer k is the needed count of HUIs to be extracted out of the quality itemsets. The algorithm does not require the user to set the minimum utilization threshold .The closure property is merged with the pruning process and improves the productivity. The results transparently show that the k-closed high utility itemsets generated using this algorithm are productive, profitable and very concise when compared with existing approaches.

Mining of top-k high utility itemsets with negative utility

High utility itemset mining (HUIM) with negative utility is an emerging data mining task. However, the setting of the minimum utility threshold is always a challenge when mining high utility itemsets (HUIs) with negative items. Although the top-k HUIM method is very common, this method can only mine itemsets with positive items, and the problem of missing itemsets occurs when mining itemsets with negative items. To solve this problem, we first propose an effective algorithm called THN (Top-k High Utility Itemset Mining with Negative Utility). It proposes a strategy for automatically increasing the minimum utility threshold. In order to solve the problem of multiple scans of the database, it uses transaction merging and dataset projection technology. It uses a redefined sub-tree utility value and a redefined local utility value to prune the search space. Experimental results on real datasets show that THN is efficient in terms of runtime and memory usage, and has excellent scalability. Moreover, experiments show that THN performs particularly well on dense datasets.

Top-k High Utility Itemset Mining Framework by using TKU Along with TKO Algorithms

High utility itemsets (HUIs) mining be developing point within in sequence mining, which alludes toward discovering the entire itemsets contains utility assembling consumer verified slightest utility farthest point minutil. In any case, setting minutil fittingly is a problematic issue for customers. When in doubt, discovering reasonable smallest amount utility farthest point through testing be repetitive methodology intended for customers. On off chance to minutil be place excessively small, such countless HUIs determination exist made, which may make mining technique survive uncommonly incompetent. Additional supply taking place, condition minutil be place unnecessarily, we do not find any HUI. Within term document, we deal with the exceeding concerns through planning an additional composition in favor of top-k high utility itemset mining, where k be an ideal number of HUIs toward mined. Two arranges about viable counts named TKU (mining Top-K Utility itemsets) plus TKO (mining Top-K utility itemsets within one phase) be planned in favor of mining that itemsets not including require toward position minutil. We provide assistant relationship about two estimations among trades on top of central focuses along with obstacles. Precise appraisals lying on together authentic as well as made datasets shows introduction about proposed computations be close to the perfect case about top tier utility mining estimations.

A Systematic Survey on High Utility Itemset Mining

High utility itemset mining considers unit profits and quantities of items in a transaction database to extract more applicable and more useful association rules. Downward closure property, which causes significant pruning in frequent itemset mining, is not established in the utility of itemsets and so the mining problem will require alternative solutions to reduce its search space and to enhance its efficiency. Using an anti-monotonic upper bound of the utility function and exploiting efficient data structures for storing and compacting the dataset to perform efficient pruning strategies are the main solutions to address high utility itemset mining problem. Different mining methods and techniques have attempted to improve performance of extracting high utility itemsets and their several variants, including high-average utility itemsets, top-k high utility itemsets, and high utility itemsets with negative values, using more efficient data structures, more appropriate anti-monotonic upper bounds, and stronger pruning strategies. This paper aims to represent a comprehensive systematic review for high utility itemset mining techniques and to classify them based on their problem-solving approaches.

Mining frequent and top-K High Utility Time Interval-based Events with Duration patterns

Traditional frequent sequential pattern mining only considers the time point-based item or event in the patterns. However, in many application, the events may span over multiple time points and the relations among events are also important. Time interval-based pattern mining is proposed to mine the interesting patterns of events that span over some time periods and also by considering the relations among events. Previous works of time interval-based pattern mining focused on the relations between events without considering the duration of each event. However, the same event with different time duration may cause different results. In this work, we propose two algorithms, SARA and SARS, for mining frequent time interval-based events with duration, TIED, patterns. TIED patterns not only keep the relations between two events but also reveal the time periods when each event happens and ends. For the performance evaluation, we propose a naive algorithm and modify a previous algorithm along with the implementation of SARA and SARS. The experimental results show that SARA and SARS are more efficient in terms of execution time and memory usage than other two algorithms. Moreover, we extend this work by considering utility value or importance of event in each time stamp. Therefore, we propose another new High Utility Time Interval-based Events with Duration, HU-TIED, pattern. HU-TIED incorporates the concept of utility pattern mining and TIED pattern mining. We design an algorithm, LMSpan, to mine top-K HU-TIED patterns. For the performance evaluation, we design a baseline algorithm, GenerateNCheck to compare with LMSpan. LMSpan takes less time and memory and generates less candidates than GenerateNCheck.

A Novel Fault Prevention Model for Metro Overhead Contact System

As an indispensable part of metro traction power supply system, the metro overhead contact system (OCS) is complex in composition and has various causes of faults, so its fault prevention is too complicated to be handled by using traditional methods. However, over the last few years, the fault data of metro OCS has been through explosive growth. Consequently, the method of high utility mining, which is capable of pointedly mining fault association rules from historical fault data, is adapted to address the issue. According to the fault association rules, once the precursor of the specified fault occurs, we can immediately conduct preventive maintenance to achieve the purpose of fault prevention. In this paper, we proposed a novel fault prevention model for metro OCS. First, we proposed a concept hierarchical database structure with a detailed encoding method, which can efficiently store the massive fault data and facilitate the subsequent mining. Then an improved algorithm for mining multiple-level top-K high utility fault-sets (MTHU) for metro OCS is proposed. At last, a case study is conducted and the mining result is analyzed to reveal the inner connections between faults. Based on the analysis result, the detailed preventive maintenance suggestions are given to effectively achieve fault prevention for OCS.

Mining Top-k Regular High-Utility Itemsets in Transactional Databases

Mining high-utility itemsets is an important task in the area of data mining. It involves exponential mining space and returns a very large number of high-utility itemsets. In a real-time scenario, it is often sufficient to mine a small number of high-utility itemsets based on user-specified interestingness. Recently, the temporal regularity of an itemset is considered as an important interesting criterion for many applications. Methods for finding the regular high utility itemsets suffers from setting the threshold value. To address this problem, a novel algorithm called as TKRHU (Top k Regular High Utility Itemset) Miner is proposed to mine top-k high utility itemsets that appears regularly where k represents the desired number of regular high itemsets. A novel list structure RUL and efficient pruning techniques are developed to discover the top-k regular itemsets with high profit. Efficient pruning techniques are designed for reducing search space. Experimental results show that proposed algorithm using novel list structure achieves high efficiency in terms of runtime and space.

TKEH: an efficient algorithm for mining top-k high utility itemsets

High utility itemsets mining is a subfield of data mining with wide applications. Although the existing high utility itemsets mining algorithms can discover all the itemsets satisfying a given minimum utility threshold, it is often difficult for users to set a proper minimum utility threshold. A smaller minimum utility threshold value may produce a huge number of itemsets, whereas a higher one may produce a few itemsets. Specification of minimum utility threshold is difficult and time-consuming. To address these issues, top-k high utility itemsets mining has been defined where k is the number of high utility itemsets to be found. In this paper, we present an efficient algorithm (named TKEH) for finding top-k high utility itemsets. TKEH utilizes transaction merging and dataset projection techniques to reduce the dataset scanning cost. These techniques reduce the dataset when larger items are explored. TKEH employs three minimum utility threshold raising strategies. We utilize two strategies to prune search space efficiently. To calculate the utility of items and upper-bounds in linear time, TKEH utilizes array-based utility technique. We carried out some extensive experiments on real datasets. The results show that TKEH outperforms the state-of-the-art algorithms. Moreover, TKEH always performs better for dense datasets.