Pattern Growth Algorithm Research Articles

An efficient and optimised frequent pattern mining using novel multipath-graph structure

The frequent pattern mining is one of the most focused areas in the data mining domain. The frequent pattern growth (FP-growth) algorithm introduced compact prefix-based data structure, frequent pattern tree (FP-Tree), to store frequent itemsets in compressed format. The FP-growth attempts to overcome drawback of candidate generation approach of multiple database scan. This work aims to propose a novel optimised data structure multipath-Graph (MP-Graph) for improving memory utilisation and efficiency of mining algorithms. The MP-Graph is a compact graph structure to store frequent patterns in memory. It generates graph nodes equal to number of frequent 1-itemsets of transaction database. Further, it stores multiple occurrences of prefix subpaths along the edges of the graph in the form of transaction bitmap instead of storing frequency of individual item node. The proposed structure helps to mine frequent patterns without constructing conditional FP-Trees. The performance of the MP-Graph mining algorithm is compared with FP-growth, CT-PRO and IFP-growth. The experimental results show order of magnitude improvement in memory consumption to store frequent patterns, nodes generated and time complexity.

APRICOIN: An Adaptive Approach for Prioritizing High-Risk Containers Inspections

Risk evaluation of the containers remains a difficult task, often due to incomplete or ambiguous information on containers. In addition, the evaluation process needs to be adapted on an ongoing basis to cope with emerging risk factors. Furthermore, high-risk container inspection is commonly hindered by a low inspection capacity, which leads to a major issue: how can we prioritize the container inspection if the number of suspect containers exceeds the inspection capacity? Container inspection prioritizing may be the answer. In this paper, we propose a novel approach for adaptively prioritizing container inspection (APRICOIN). First, we enhance the container information flow to alleviate the problem of incomplete information by proposing an enhanced container descriptive. Second, we introduce the APRICOIN algorithm, which combines frequent pattern mining and a fuzzy logic system, to assess the container’s risk score. The frequent pattern growth algorithm is proposed to retrieve the key criteria for evaluating container risk. This is done through mining frequent criteria sets within the historic data set of container inspections by customs. The mined frequent criteria sets are used to assess fuzzy inference rules which are periodically readjusted to integrate new key criteria. Thereafter, the fuzzy logic system uses the obtained fuzzy inference rules to calculate a container’s risk score. Our major contribution consists of providing a new adaptive approach for assessing a container’s risk through combining frequent criteria mining and fuzzy logic. An illustrative study and a comparison with alternative approaches are performed to validate the proposed algorithm.

Discovering partial periodic-frequent patterns in a transactional database

Proposed a novel model to find partial periodic-frequent patterns in a database.Introduced a measure to find partial periodic-frequent patterns in a database.An efficient pruning technique has been proposed to reduce the computational cost.Described a pattern-growth algorithm to find all partial periodic-frequent patternsExperimental results show that our model is efficient. Time and frequency are two important dimensions to determine the interestingness of a pattern in a database. Periodic-frequent patterns are an important class of regularities that exist in a database with respect to these two dimensions. Current studies on periodic-frequent pattern mining have focused on discovering full periodic-frequent patterns, i.e., finding all frequent patterns that have exhibited complete cyclic repetitions in a database. However, partial periodic-frequent patterns are more common due to the imperfect nature of real-world. This paper proposes a flexible and generic model to find partial periodic-frequent patterns. A new interesting measure, periodic-ratio, has been introduced to determine the periodic interestingness of a frequent pattern by taking into account its proportion of cyclic repetitions in a database. The proposed patterns do not satisfy the anti-monotonic property. A novel pruning technique has been introduced to reduce the search space effectively. A pattern-growth algorithm to find all partial periodic-frequent patterns has also been presented in this paper. Experimental results demonstrate that the proposed model can discover useful information, and the algorithm is efficient.

Providing Privateness via Transaction Splitting Using PFP Growth Algorithm

Frequent itemset mining is one of the most necessary problems in data extraction. The chance of devious a discrepancy private frequent itemset mining algorithm which can not only accomplish high data usefulness and a high level of secrecy, but also offer high time effectiveness. To this end, offer a discrepancy frequent itemset mining algorithm based on the Frequent Pattern-growth algorithm, which is referred to as Private Frequent Pattern-growth. The Private frequent Pattern -growth algorithm consists of a preprocessing phase and a mining phase. To improve the utility and secrecy tradeoff, a innovative smart splitting method is proposed to change the database in preprocessing phase. It needs to be performed only once for a given database. To compensate the information loss caused by transaction splitting, To estimate the definite support of itemsets in the original database in mining segment utilize run time estimation method. In accumulation, develop a dynamic reduction method to dynamically reduce the amount of noise added to guarantee privacy during the extracting process by leveraging the downward closure property. Private common pattern-growth algorithm is shown it is e-differentially private through formal privacy analysis; explain that PFP-growth algorithm is e- discrepancy secrecy. Extensive experiments on real datasets exemplify that our PFP-growth algorithm considerably outperforms the state-of-the-art techniques.

Differentially Private Utility Item Mining

In today's business world there is excess of available data & a great need to make good use of it. Data mining is art of extracting pattern and knowledge from large amount of data. Frequent itemset plays essential role in many Data Mining tasks that try to find out interesting patterns from database such as association rules. Association rule mining is a finding association among large databases variables. Mining of frequent itemset is most popular problem in data mining. The frequent itemsets recognition is valuable for economic & research purpose. But valuable discovered frequent itemsets should not only assure security but also achieve high data utility & offer time efficiency. The frequent itemsets are patterns or items like itemset, substructures or subsequences that comes out in data set frequently. There are several Frequent Item mining algorithms for frequent item mining such as Apriori, Frequent Pattern growth, Eclat, Utility Pattern growth algorithms. To provide security or privacy here we use differentially private Utility Item mining algorithm using Utility Pattern- growth algorithm. It consists of Preprocessing & mining phase. In preprocessing phase, to enhance utility & privacy advance smart splitting method is proposed to transform database. For given Database preprocessing phase should be performed only once. In mining phase run time estimation & dynamic reduction performed. To cover the information loss by smart splitting, we contrive run time estimation to calculate actual support of itemsets in original database. For privacy we have added noise in the database, we put forward dynamic reduction method to reduce the noise dynamically which guarantees privacy during mining process. In this paper we proposed new algorithm for mining high utility itemsets called as UP growth which consider not only frequency of itemset but also utility associated with the itemset. Keywords— Frequent Item Mining, є- differential privacy, FP- growth, UP-growth.

FP-Split SPADE-An Algorithm for Finding Sequential Patterns

Sequential Pattern Mining (SPM) is one of the key areas in Web Usage Mining (WUM) with broad applications such as analyzing customer behavior from weblog files. The current algorithms in this area can be classified into two broad areas, namely, apriori-based and pattern-growth based. Apriori based algorithms for mining sequential patterns need to scan the database many times as they focus on candidate generation and test approach. A lot of research has been done so far, but even the best apriori based algorithm for SPM in terms of number of database scans is SPADE that scans the database three times for discovering sequential patterns. Pattern growth based algorithms avoid the candidate generation step and the best pattern growth algorithm known so far is Prefix Span that needs to scan the database at least twice. In this paper, a novel algorithm for SPM is proposed called FP-Split SPADE that reduced the database scan to only one by creating an FP-Split tree and applying SPADE algorithm on the tree instead on sequence database that greatly improved the efficiency of mining sequential patterns.

Incorporating User’s Preferences into Scholarly Publications Recommendation

Over the years, there has been increasing growth in academic digital libraries. It has therefore become overwhelming for researchers to determine important research materials. In most existing research works that consider scholarly paper recommendation, the researcher’s preference is left out. In this paper, therefore, Frequent Pattern (FP) Growth Algorithm is employed on potential papers generated from the researcher’s preferences to create a list of ranked papers based on citation features. The purpose is to provide a recommender system that is user oriented. A walk through algorithm is implemented to generate all possible frequent patterns from the FP-tree after which an output of ordered recommended papers combining subjective and objective factors of the researchers is produced. Experimental results with a scholarly paper recommendation dataset show that the proposed method is very promising, as it outperforms recommendation baselines as measured with nDCG and MRR.

Association Rule Mining with Parallel Frequent Pattern Growth Algorithm on Hadoop

Association Rule Mining with Parallel Frequent Pattern Growth Algorithm on Hadoop

Association Rule Mining on Five Years of Motor Vehicle Crashes

Every year, road accidents kill more than a million people and injure more than 20 million worldwide. This paper aims to offer guidance on road safety and create awareness by pinpointing the major causes of traffic accidents. The study investigates motor vehicle crashes in the Genesee Finger Lakes Region of New York State. Frequency Pattern Growth algorithm is utilized to cultivate knowledge and create association rules to highlight the time and environment settings that cause the most catastrophic crashes. This knowledge can be used to warn drivers about the dangers of accidents, and how the consequences are worse given a specific context. For instance, a discovered rule from the data states that ‘most of the crashes occur between 12:00 pm and 6:00pm’; hence, it is suggested to modify existing navigation application to warn drivers about the increase in risk factor.

An Enhanced Frequent Pattern Analysis Technique from the Web Log Data

improve user experience while accessing the, website. Web usage mining is used to evaluate user's previous experiences, which helps to improve functionality of that website. In this paper a technique for web usage mining is proposed, which extends features of synaptic search and Frequent Pattern Growth algorithm. Proposed technique uses synaptic search property to search data on web on the basis of location and uses FP growth algorithm to generate results.

Efficient discovery of periodic-frequent patterns in very large databases

Periodic-frequent patterns (or itemsets) are an important class of regularities that exist in a transactional database. Finding these patterns involves discovering all frequent patterns that satisfy the user-specified maximum periodicity constraint. This constraint controls the maximum inter-arrival time of a pattern in a database. The time complexity to measure periodicity of a pattern is O(n), where n represents the number of timestamps at which the corresponding pattern has appeared in a database. As n usually represents a high value in voluminous databases, determining the periodicity of every candidate pattern in the itemset lattice makes the periodic-frequent pattern mining a computationally expensive process. This paper introduces a novel approach to address this problem. Our approach determines the periodic interestingness of a pattern by adopting greedy search. The basic idea of our approach is to discover all periodic-frequent patterns by eliminating aperiodic patterns based on suboptimal solutions. The best and worst case time complexities of our approach to determine the periodic interestingness of a frequent pattern are O(1) and O(n), respectively. We introduce two pruning techniques and propose a pattern-growth algorithm to find these patterns efficiently. Experimental results show that our algorithm is runtime efficient and highly scalable as well.

The Research on Personalized Recommendation Algorithm of Library Based on Big Data and Association Rules

In order to provide better information consistent with their preferences features for users, personalized recom- mendation technology has become an important research field of digital libraries and get more and more attention from searchers. Among them, the large data mining and association rules-based personalized recommendation technology is the focus of research in the field of recommendation. In this paper, these two issues are studied. In order to increase the lend- ing rate of collections, this paper use association rules analyzes for borrowing pattern mining, to obtain library users inter- ests, to analyze different types of readers' purpose library collections, and automatically provide readers with other books related to such book. Through improved frequent pattern growth algorithm, combined with online recommended and off- line recommendation method, achieved a more satisfactory recommendation results. Finally, taken experimental analysis and verification for these techniques studies, and future research were discussed.

A Fuzzy Frequent Pattern-Growth Algorithm for Association Rule Mining

A Fuzzy Frequent Pattern-Growth Algorithm for Association Rule Mining

A Novel Algorithm to Diagnosis Type II Diabetes Mellitus Based on Association Rule Mining Using MPSO-LSSVM with Outlier Detection Method

Background/Objectives: The execution of Frequent Pattern Growth algorithm on medical data is difficult. Association rule based classification is an interesting area focused that can be utilized for early diagnosis. Methods/Statistical analysis: Discretization phase is necessary to transform numerical characteristics. The results are given to Complete Frequent Patten Growth++ for the purpose of rule induction. Accordingly, using Modified Particle Swarm Optimization together with Least Squares Support Vector Machine scheme (MPSO-LSSVM) rules are produced with outlier detection method. Pima Indians Diabetes Data Set is taken as an input. The execution time, number of rules generation and the detection of outlier percentage are analyzed. Results: The CFP-growth algorithm utilizes for finding frequent patterns where constructing the Minimum Item Support (MIS)-tree, CFP-array and producing frequent patterns from the MIS-tree. From the set of frequent item sets found, create all the association rules that have a confidence exceeding the minimum confidence. The Enhanced outlier detection method is used for determining the outlier degree from association rules for outlier detection. Association rules are mined using MPSO-LSSVM classification based association rule mining algorithm. The classification based association rule generation using MPSO-LSSVM is utilized first time in this work with outlier detection method. For the reason of eradicating the effect of unavoidable outliers in investigation sample on a scheme’s performance, a new MPSO-LSSVM with the integration of outlier detection method is proposed first time. The experimental observations reveal that this framework provides a better accuracy of 95% when evaluated against the existing techniques. Conclusion/Application: CFP-Growth++ proposed for rule pruning and MPSO-LSSVM based algorithm used for mining association rules from Type-2 DM dataset. This work is suitable for early detection of type-2 diabetes mellitus disease.

An Anonymous Email Identification Solution based on Writing Structural Patterns

A method to analyze anonymous emails in digital forensics is presented in this paper. The frequent pattern-growth algorithm is used in the proposed method to analyze an email and obtain the structural email writing pattern of the user. The influence of a user's writing structural pattern on the analysis of an anonymous email varies. The analytic hierarchy process is used to calculate the weight of a user's different writing structural patterns. For a given anonymous email, matching the writing structural pattern and weight calculation can help investigators improve their decision making and determine the author of an anonymous email in forensic work.

An Optimized Algorithm for Association Rule Mining Using FP Tree

Data mining is used to deal with the huge size of the data stored in the database to extract the desired information and knowledge. It has various techniques for the extraction of data; association rule mining is the most effective data mining technique among them. It discovers hidden or desired pattern from large amount of data. Among the existing techniques the frequent pattern growth (FP growth) algorithm is the most efficient algorithm in finding out the desired association rules. It scans the database only twice for the processing. The issue with the FP growth algorithm is that it generates a huge number of conditional FP trees. In the proposed work, we have designed a new technique which mines out all the frequent item sets without the generation of the conditional FP trees. Unlike FP tree it scans the database only once which reduces the time efficiency of the algorithm. It also finds out the frequency of the frequent item sets to find out the desired association rules.

An Asynchronous Periodic Sequential Pattern Mining Algorithm with Multiple Minimum Item Supports for Ad Hoc Networking

The original sequential pattern mining model only considers occurrence frequencies of sequential patterns, disregarding their occurrence periodicity. We propose an asynchronous periodic sequential pattern mining model to discover the sequential patterns that not only occur frequently but also appear periodically. For this mining model, we propose a pattern-growth mining algorithm to mine asynchronous periodic sequential patterns with multiple minimum item supports. This algorithm employs a divide-and-conquer strategy to mine asynchronous periodic sequential patterns in a depth-first manner recursively. We describe the process of algorithm realization and demonstrate the efficiency and stability of the algorithm through experimental results.

Genetic learning of the membership functions for mining fuzzy association rules from low quality data

Many methods have been proposed to mine fuzzy association rules from databases with crisp values in order to help decision-makers make good decisions and tackle new types of problems. However, most real-world problems present a certain degree of imprecision. Various studies have been proposed to mine fuzzy association rules from imprecise data but they assume that the membership functions are known in advance and it is not an easy task to know a priori the most appropriate fuzzy sets to cover the domains of the variables. In this paper, we propose FARLAT-LQD, a new fuzzy data-mining algorithm to obtain both suitable membership functions and useful fuzzy association rules from databases with a wide range of types of uncertain data. To accomplish this, first we perform a genetic learning of the membership functions based on the 3-tuples linguistic representation model to reduce the search space and to learn the most adequate context for each fuzzy partition, maximizing the fuzzy supports and the interpretability measure GM3M in order to preserve the semantic interpretability of the obtained membership functions. Moreover, we propose a new algorithm based on the Fuzzy Frequent Pattern-growth algorithm, called FFP-growth-LQD, to efficiently mine the fuzzy association rules from inaccurate data considering the learned membership functions in the genetic process. The results obtained over 3 databases of different sizes and kinds of imprecisions demonstrate the effectiveness of the proposed algorithm.

A node linkage approach for sequential pattern mining.

Sequential Pattern Mining is a widely addressed problem in data mining, with applications such as analyzing Web usage, examining purchase behavior, and text mining, among others. Nevertheless, with the dramatic increase in data volume, the current approaches prove inefficient when dealing with large input datasets, a large number of different symbols and low minimum supports. In this paper, we propose a new sequential pattern mining algorithm, which follows a pattern-growth scheme to discover sequential patterns. Unlike most pattern growth algorithms, our approach does not build a data structure to represent the input dataset, but instead accesses the required sequences through pseudo-projection databases, achieving better runtime and reducing memory requirements. Our algorithm traverses the search space in a depth-first fashion and only preserves in memory a pattern node linkage and the pseudo-projections required for the branch being explored at the time. Experimental results show that our new approach, the Node Linkage Depth-First Traversal algorithm (NLDFT), has better performance and scalability in comparison with state of the art algorithms.

Comparative Study of Various Sequential Pattern Mining Algorithms

In Sequential pattern mining represents an important class of data mining problems with wide range of applications. It is one of the very challenging problems because it deals with the careful scanning of a combinatorially large number of possible subsequence patterns. Broadly sequential pattern ming algorithms can be classified into three types namely Apriori based approaches, Pattern growth algorithms and Early pruning algorithms. These algorithms have further classification and extensions. Detailed explanation of each algorithm along with its important features, pseudo code, advantages and disadvantages is given in the subsequent sections of the paper. At the end a comparative analysis of all the algorithms with their supporting features is given in the form of a table. This paper tries to enrich the knowledge and understanding of various approaches of sequential pattern mining. General Terms Sequential Pattern Mining, Subsequence detection, Candidate pruning.