Dense Databases Research Articles

An efficient strategy for mining high-efficiency itemsets in quantitative databases

The classic problems in itemset mining involve finding frequent itemsets and high-utility itemsets. However, frequent itemset mining has the disadvantage of not paying attention to the profit of products, while high-utility itemset mining does not address the issue of the cost price of the products. Therefore, neither can locate products with high-efficiency value on investment. To overcome these problems, the high-efficiency itemset mining (HEIM) problem was proposed. Despite its practicality, this issue has received little attention. The algorithms proposed to exploit high-efficiency itemsets (HEI) still use ineffective strategies on dense databases and unstrict upper bounds, requiring a lot of time and memory. To address the current issues with HEIM, the paper proposes tight upper bounds for the early pruning of candidates. Several techniques are also proposed, such as combining similar transactions and saving promising transaction locations, to reduce the cost of database scanning. Finally, the techniques are combined to propose a novel way to implement the MHEI (an efficient strategy for Mining High-Efficiency Itemsets in quantitative databases) to optimize the HEIM process. The experimental process also shows that the proposed algorithm has performance better than the state-of-the-art algorithm in HEIM.

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.

Efficient algorithms for mining closed high utility itemsets in dynamic profit databases

The problem of discovering high-utility itemsets (HUIs) in transaction databases, which is an extension of Frequent Itemset Mining, is a commonly encountered mining task. Researchers have proposed algorithms to efficiently mine highly profitable itemsets in customer transaction databases, in which the unit profits of items are fixed. However, this assumption does not reflect the true nature of the utility measure of items in real-life transaction databases, which might vary over time. Moreover, since this important characteristic is ignored by all the current HUI mining algorithms, they are either not applicable to this type of database, or they generate inaccurate results. In addition, the HUI mining algorithms’ traditional limitation is that they produce a huge number of HUIs for users. In this paper, we define the problem of mining a lossless, concise and compact representation of HUIs, called closed HUIs (CHUIs), in dynamic unit profit databases. Based on newly defined of utility measure, a novel algorithm, called iEFIM-Closed, is introduced. This relies on this new utility measure, a novel compact database format to reduce the cost of database scans and increase the efficiency of the mining process. Experimental evaluations show that iEFIM-Closed significantly outperforms state-of-the-art algorithms for mining CHUIs on sparse databases with dynamic profit, and has competitive performance in dense databases in terms of runtime, the cost of database scans and scalability.

Generating a Condensed Representation for Positive and Negative Association Rules

Given a large collection of transactions containing items, a basic common association rules problem is the huge size of the extracted rule set. Pruning uninteresting and redundant association rules is a promising approach to solve this problem. In this paper, we propose a Condensed Representation for Positive and Negative Association Rules representing non-redundant rules for both exact and approximate association rules based on the sets of frequent generator itemsets, frequent closed itemsets, maximal frequent itemsets, and minimal infrequent itemsets in database B. Experiments on dense (highly-correlated) databases show a significant reduction of the size of extracted association rule set in database B.

Frequent Itemset Mining in a Unique Sc an using Transaction Database

In recent year, frequent Itemset Mining (FIM) has occurred as a vital role in data mining tasks. The search of FIM in a transactions data is discovered in this paper, pull out hidden pattern from transactions data. The main two limitation of the Apriori algorithm are undertaken, first, its scans the complete Databases at every passes to compute the supports of every itemset produced and secondly, the user defined responsive to variation of min_sup (minimum supports) thresholds. In this paper, proposed methodology called frequent Itemset Mining in unique Scan (FIMUS), needs a scan only one time of transaction databases to extract frequent itemsets. The generation of a static numbers of candidate Itemset is an exclusive feature, individually from the threshold of min_sup, which reduces the execution time for huge database. The proposed algorithm FIMUS is compared with Apriori algorithm using benchmark database for a dense databases. The experimental result confirms the scalability of FIMUS.

Efficient algorithms for simultaneously mining concise representations of sequential patterns based on extended pruning conditions

The concise representations of sequential patterns, including maximal sequential patterns, closed sequential patterns and sequential generator patterns, play an important role in data mining since they provide several benefits when compared to sequential patterns. One of the most important benefits is that their cardinalities are generally much less than the cardinality of the set of sequential patterns. Therefore, they can be mined more efficiently, use less storage space, and it is easier for users to analyze the information provided by the concise representations. In addition, the set of all maximal sequential patterns can be utilized to recover the complete set of sequential patterns, while closed sequential patterns and sequential generators can be used together to generate non-redundant sequential rules and to quickly recover all sequential patterns and their frequencies. Several algorithms have been proposed to mine the concise representations separately, i.e., each of them has been designed to discover only a type of the concise representation. However, they remain time-consuming and memory intensive tasks. To address this problem, we propose three novel efficient algorithms named FMaxSM, FGenCloSM and MaxGenCloSM to exploit only maximal sequential patterns, to simultaneously mine both the sets of closed sequential patterns and generators, and to discover all three concise representations during the same process. To our knowledge, MaxGenCloSM is the first algorithm for concurrently mining the three concise representations of sequential patterns. The proposed algorithms are based on two novel local pruning strategies called LPMAX and LPMaxGenClo that are designed to prune non-maximal, non-closed and non-generator patterns earlier and more efficiently at two and three successive levels of the prefix tree without subsequence relation checking. Extensive experiments on real-life and synthetic databases show that FMaxSM, FGenCloSM and MaxGenCloSM are up to two orders of magnitude faster than the state-of-the-art algorithms and that the proposed algorithms consume much less memory, especially for low minimum support thresholds and for dense databases.

A new framework for mining weighted periodic patterns in time series databases

Mining periodic patterns in time series databases is a daunting research task that plays a significant role at decision making in real life applications. There are many algorithms for mining periodic patterns in time series, where all patterns are considered as uniformly same. However, in real life applications, such as market basket analysis, gene analysis and network fault experiment, different types of items are found with several levels of importance. Again, the existing algorithms generate huge periodic patterns in dense databases or in low minimum support, where most of the patterns are not important enough to participate in decision making. Hence, a pruning mechanism is essential to reduce these unimportant patterns. As a purpose of mining only important patterns in a minimal time period, we propose a weight based framework by assigning different weights to different items. Moreover, we develop a novel algorithm, WPPM (Weighted Periodic Pattern Mining Algorithm), in time series databases underlying suffix trie structure. To the best of our knowledge, ours is the first proposal that can mine three types of weighted periodic pattern, (i.e. single, partial, full) in a single run. A pruning method is introduced by following downward property, with respect of the maximum weight of a given database, to discard unimportant patterns. The proposed algorithm presents flexibility to user by providing intermediate unimportant pattern skipping opportunity and setting different starting positions in the time series sequence. The performance of our proposed algorithm is evaluated on real life datasets by varying different parameters. At the same time, a comparison between the proposed and an existing algorithm is shown, where the proposed approach outperformed the existing algorithm in terms of time and pattern generation.

Vertical Pattern Mining Algorithm for Multiple Support Thresholds

Frequent pattern mining is an important task in discovering hidden items that co-occur (itemset) more than a predefined threshold in a database. Mining frequent itemsets has drawn attention although rarely occurring ones might have more interesting insights. In existing studies, to find these interesting patterns (rare itemsets), user defined single threshold should be set low enough but this results in generation of huge amount of redundant itemsets. We present Multiple Item Support-eclat; MIS-eclat algorithm, to mine frequent patterns including rare itemsets under multiple support thresholds (MIS) by utilizing a vertical representation of data. We compare MIS-eclat to our previous tree based algorithm, MISFP-growth28 and another recent algorithm, CFP-growth++22 in terms of execution time, memory usage and scalability on both sparse and dense databases. Experimental results reveal that MIS-eclat and MISFP-growth outperform CFP-growth++ in terms of execution time, memory usage and scalability.

Scalable Indoor Localization via Mobile Crowdsourcing and Gaussian Process.

Indoor localization using Received Signal Strength Indication (RSSI) fingerprinting has been extensively studied for decades. The positioning accuracy is highly dependent on the density of the signal database. In areas without calibration data, however, this algorithm breaks down. Building and updating a dense signal database is labor intensive, expensive, and even impossible in some areas. Researchers are continually searching for better algorithms to create and update dense databases more efficiently. In this paper, we propose a scalable indoor positioning algorithm that works both in surveyed and unsurveyed areas. We first propose Minimum Inverse Distance (MID) algorithm to build a virtual database with uniformly distributed virtual Reference Points (RP). The area covered by the virtual RPs can be larger than the surveyed area. A Local Gaussian Process (LGP) is then applied to estimate the virtual RPs’ RSSI values based on the crowdsourced training data. Finally, we improve the Bayesian algorithm to estimate the user’s location using the virtual database. All the parameters are optimized by simulations, and the new algorithm is tested on real-case scenarios. The results show that the new algorithm improves the accuracy by 25.5% in the surveyed area, with an average positioning error below 2.2 m for 80% of the cases. Moreover, the proposed algorithm can localize the users in the neighboring unsurveyed area.

Finding sequential patterns with TF-IDF metrics in health-care databases

Abstract Finding frequent sequential patterns has been defined as finding ordered list of items that occur more times in a database than a user defined threshold. For big and dense databases that contain really long sequences and large itemset such as medical case histories, algorithm proposed on this idea of counting the occurrences output enourmous number of highly redundant frequent sequences, and are therefore simply impractical. Therefore, there is a need for algorithm that perform frequent pattern search and prefiltering simultaneously. In this paper, we propose an algorithm that reinterprets the term support on text mining basis. Experiments show that our method not only eliminates redundancy among the output sequences, but it scales much better with huge input data sizes. We apply our algorithm for mining medical databases: what diagnoses are likely to lead to a certain future health condition.

Novel parallel method for association rule mining on multi-core shared memory systems

ShaFEM: a novel association rule mining method for multi-core shared memory systems.ShaFEM self-adapts to data characteristic to run fast on sparse and dense databases.ShaFEM uses two mining strategies and dynamically switching between them.ShaFEM applies its new lock free solution, new data structure named XFP-tree.ShaFEM is up to 5.8 times faster and 7.1 times less memory than the compared method. Association rule mining (ARM) is an important task in data mining with many practical applications. Current methods for association rule mining have shown unstable performance for different database types and under-utilize the benefits of multi-core shared memory machines. In this paper, we address these issues by presenting a novel parallel method for finding frequent patterns, the most computational intensive phase of ARM. Our proposed method, named ShaFEM, combines two mining strategies and applies the most appropriate one to each data subset of the database to efficiently adapt to the data characteristics and run fast on both sparse and dense databases. In addition, our newlock-free design minimizes the synchronization needs and maximizes the data independence to enhance the scalability. The new structure lends itself well to dynamic job scheduling resulting in a well-balanced load on the new multi-core shared memory architectures. We have evaluated ShaFEM on 12-core multi-socket servers and found that our method run up to 5.8 times faster and consumes memory up to 7.1 times less than the state-of-the-art parallel method. For some test cases, ShaFEM can save up to 4.9days of execution time over the compared method.

Efficient Algorithms for Mining Frequent Patterns from Sparse and Dense Databases

AbstractIn this article, we present a new approach for frequent pattern mining (FPM) that runs fast for both sparse and dense databases. Two algorithms, FEM and DFEM, based on our approach are also introduced. FEM applies a fixed threshold as the condition for switching between the two mining strategies; meanwhile, DFEM adopts this threshold dynamically at runtime to best fit the characteristics of the database during the mining process, especially when minimum support threshold is low. Additionally, we present optimization techniques for the proposed algorithms to speed the mining process, reduce the memory usage, and optimize the I/O cost. We also analyze in depth the performance of FEM and DFEM and compare them with several existing algorithms. The experimental results show that FEM and DFEM achieve a significant improvement in execution time and consume less memory than many popular FPM algorithms including the well-known Apriori, FP-growth, and Eclat.

Research Methods in Child Language: A Practical Guide. By ErikaHoff, ed. First Edition. Blackwell Publishing Ltd., 2012. 362 pp. $39.72 (paperback).

Research Methods in Child Language: A Practical Guide. By ErikaHoff, ed. First Edition. Blackwell Publishing Ltd., 2012. 362 pp. $39.72 (paperback).

Clustering local frequency items in multiple databases

Frequent items could be considered as a basic type of patterns in a database. In the context of multiple data sources, most of the global patterns are based on local frequency items. A multi-branch company transacting from different branches often needs to extract global patterns from data distributed over the branches. Global decisions could be taken effectively using such patterns. Thus, it is important to cluster local frequency items in multiple databases. An overview of the existing measures of association is presented here. For the purpose of selecting the suitable technique of mining multiple databases, we have surveyed the existing multi-database mining techniques. A study on the related clustering techniques is also covered here. The notion of high frequency itemsets is introduced here, and an algorithm for synthesizing supports of such itemsets is designed. The existing clustering technique might cluster local frequency items at a low level, since it estimates association among items in an itemset with a low accuracy, and thus a new algorithm for clustering local frequency items is proposed. Due to the suitability of measure of association A2, association among items in a high frequency itemset is synthesized based on it. The soundness of the clustering technique has been shown. Numerous experiments are conducted using five datasets, and the results on different aspects of the proposed problem are presented in the experimental section. The effectiveness of the proposed clustering technique is more visible in dense databases.

An adaptive approach to mining frequent itemsets efficiently

The mining of frequent itemsets is a fundamental and important task of data mining. To improve the efficiency in mining frequent itemsets, many researchers developed smart data structures to represent the database, and designed divide-and-conquers approaches to generate frequent itemsets from these data structures. However, the features of real databases are diversified and the features of local databases in the mining process may also change. Consequently, different data structures may be utilized in the mining process to enhance efficiency. This study presents an adaptive mechanism to select suitable data structures depending on database densities: the Frequent Pattern List (FPL) for sparse databases, and the Transaction Pattern List (TPL) for dense databases. Experimental results verified the effectiveness of this approach.

TripleEye: Mining Closed Itemsets with Minimum Length Thresholds Based on Ordered Inclusion Tree

Itemset mining is one of the most essential tasks in the field of data mining. In this paper, we focus on minimum length as a mining measure for closed itemset mining. That is, our task is formalized as follows: Given a database and user-specified minimum length threshold, find all closed itemsets whose length is at least the minimum length. Closed itemset mining based on the minimum length threshold is preferable when it is difficult for users to determine the appropriate minimum support value. For our task, we propose TripleEye: an efficient algorithm of closed itemset mining that is based on the intersection of transactions in a database. Our algorithm utilizes the information of inclusion relations between itemsets to avoid the generation of duplicate itemsets and reduce the computational cost of intersection. During the mining procedure, the information of inclusion relations is maintained in a novel tree structure called Ordered Inclusion Tree. Experiments show that our algorithm dramatically reduces the computational cost, compared against naive intersection-based algorithm. Our algorithm also achieves up to twice the running speed of conventional algorithms given dense databases.

Mining Sequential Patterns in Dense Databases

Sequential pattern mining is an important data mining problem with broad applications, including the analysis of customer purchase patterns, Web access patterns, DNA analysis, and so on. We show on dense databases, a typical algorithm like Spade algorithm tends to lose its efficiency. Spade is based on the used of lists containing the localization of the occurrences of pattern in the sequences and these lists are not appropriated in the case of dense databases. In this paper we present an adaptation of the wellknown diffset data representation [12] with Spade algorithm. The new version is called dSpade. Since diffset shows high performance for mining frequent itemsets in dense transactional databases, experimental evaluation shows that dSpade is suitable for mining dense sequence databases.

Combination of Two Evolutionary Methods for Mining Association Rules in Large and Dense Databases

Among several methods of extracting association rules that have been reported, a new evolutionary method named Genetic Network Programming (GNP) has also shown its effectiveness for small databases in the sense that they have a relatively small number of attributes. However, this conventional GNP method is not be able to deal with large databases with a huge number of attributes, because its search space becomes very large, causing bad performance at running time. The aim of this paper is to propose a new method to extract association rules from large and dense databases with a huge amount of attributes through the combination of conventional GNP based mining method and a specially designed genetic algorithm (GA). Each of these evolutionary methods works in its own processing level and they are highly synchronized to act as one system.Our strategy consists in the division of a large and dense database into many small databases. These small databases are considered as individuals and form a population. Then the conventional GNP based mining method is applied to extract association rules for each of these individuals. Finally, the population is evolved through several generations using GA with special genetic operators considering the acquired information. Two complementary processing levels are defined: Global Level and Local Level, each with its own independent tasks and processes. In the Global Level mainly GA process is carried out, whereas in the Local Level, conventional GNP based mining method is carried out in parallel and they generate their own local pools of association rules. Several special genetic operations for GA in the Global Level are proposed and the performance of each of them and their combination is shown and compared.In our simulations, the conventional GNP based mining method and our proposed method are compared using a real world large and dense database with a huge amount of attributes. The results show that extending the conventional GNP based mining method using GA allows to extract association rules from large and dense databases directly and more efficiently than the conventional GNP method.

On pushing weight constraints deeply into frequent itemset mining

There have been many studies on mining frequent itemset (or pattern) in the data mining field because of its broad applications in mining association rules, correlations, graph patterns, constraint based frequent patterns, sequential patterns, and many other data mining tasks. One of major challenges in frequent pattern mining is a huge number of result patterns. As the minimum threshold becomes lower, an exponentially large number of itemsets are generated. Therefore, pruning unimportant patterns effectively in mining process is one of main topics in frequent pattern mining. In weighted frequent pattern mining, not only support but also weight are used and important patterns can be detected. In this paper, we propose two efficient algorithms for mining weighted frequent itemsets in which the main approaches are to push weight constraints into the Apriori algorithm and the pattern growth algorithm respectively. Additionally, we show how to maintain the downward closure property in mining weighted frequent itemsets. In our approach, the normalized weights within the weight range are used according to the importance of items. A weight range is used to restrict weights of items and a minimum weight is utilized to balance between weight and support of items for pruning the search space. Our approach generates fewer but important weighted frequent itemsets in large databases, particularly dense databases with low minimum supports. An extensive performance study shows that our algorithm outperforms previous mining algorithms. In addition, it is efficient and scalable.

Comparative Association Rules Mining Using Genetic Network Programming (GNP) with Attributes Accumulation Mechanism and its Application to Traffic Systems

Among several methods of extracting association rules that have been reported, a new evolutionary method named Genetic Network Programming (GNP) has also shown its effectiveness for dense databases. However, the conventional GNP data mining method can not find comparative relations and hidden patterns among a large amount of data. In this paper, we present a method of comparative association rules mining using Genetic Network Programming (GNP) with attributes accumulation mechanism in order to uncover comparative association rules between different datasets. GNP is an evolutionary approach recently developed, which can evolve itself and find the optimal solutions. The objective of the comparative association rules mining is to check two or more databases instead of one, so as to find the hidden relations among them. The proposed method measures the importance of association rules by using the absolute values of the confidence differences of the rules obtained from different databases and can get a number of interesting rules. Association rules obtained by comparison can help us to find and analyze the explicit and implicit patterns among a large amount of data. On the other hand, the calculation is very time-consuming, when the conventional GNP based data mining is used for the large attributes case. So, we have proposed an attributes accumulation mechanism to improve the performances. Then, the comparative association rules mining using GNP has been applied to a complicated traffic system. By mining and analyzing the rules under different traffic situations, it was found that we can get interesting information of the traffic system.