Pattern mining using Linked list (PML) mine the frequent patterns from transaction dataset using Linked list data structure

Closed frequent similar pattern mining: Reducing the number of frequent similar patterns without information loss

Frequent pattern mining is a classic data mining technique, generally applicable to a wide range of application domains, and a mature area of research. The fundamental challenge arises from the combinatorial nature of frequent itemsets, scaling exponentially with respect to the number of unique items. Apriori-based and FPTree-based algorithms have dominated the space thus far. Initial phases of this research relied on the Apriori algorithm and utilized a distributed computing environment; we proposed the Cartesian Scheduler to manage Apriori's candidate generation process. To address the limitation of bottom-up frequent pattern mining algorithms such as Apriori and FPGrowth, we propose the Frequent Hierarchical Pattern Tree (FHPTree): a tree structure and new frequent pattern mining paradigm. The classic problem is redefined as frequent hierarchical pattern mining where the goal is to detect frequent maximal pattern covers. Under the proposed paradigm, compressed representations of maximal patterns are mined using a top-down FHPTree traversal, FHPGrowth, which detects large patterns before their subsets, thus yielding significant reductions in computation time. The FHPTree memory footprint is small; the number of nodes in the structure scales linearly with respect to the number of unique items. Additionally, the FHPTree serves as a persistent, dynamic data structure to index frequent patterns and enable efficient searches. When the search space is exponential, efficient targeted mining capabilities are paramount; this is one of the key contributions of the FHPTree. This dissertation will demonstrate the performance of FHPGrowth, achieving a 300x speed up over state-of-the-art maximal pattern mining algorithms and approximately a 2400x speedup when utilizing FHPGrowth in a distributed computing environment. In addition, we allude to future research opportunities, and suggest various modifications to further optimize the FHPTree and FHPGrowth. Moreover, the methods we offer will have an impact on other data mining research areas including contrast set mining as well as spatial and temporal mining.

Parallel laying of high-voltage cables will generate a zero-sequence current, due to spatial electromagnetic induction, which reduces the cable’s current-carrying capacity, causing heating and corrosion of the grounding points and deteriorating grounding performance. Currently, there is a lack of effective control measures. This article establishes a calculation model for the cable sheath current under the condition of double circuit cable cross interconnection grounding, analyzes the causes of a zero-sequence grounding current in a double circuit cable sheath, and proposes an optimal phase sequence selection method, considering load changes with the goal of maximizing the probability of the cable sheath current, not exceeding the standard. The results show that when the double circuit cable is evenly distributed in the cross interconnection section, the zero-sequence grounding current will be generated on the metal sheath of the cable, causing an excessive total grounding current. By applying the proposed probability-based phase-sequence optimization, the likelihood that both circuits simultaneously satisfy the sheath-current criterion can be significantly improved; for example, under representative layouts and load distributions, the “both-within-limit” probability can reach 53.3% (horizontal layout), 76.2% (horizontal equilateral triangle layout), 90.5% (vertical layout), and 81.6% (vertical equilateral triangle layout). For different working conditions, selecting the optimal load phase sequence combination by maximizing the probability of the sheath current and not exceeding the standard within the current carrying area can help to reduce the cable sheath current.

Frequent pattern mining is a basic problem in data mining and knowledge discovery. The discovered patterns can be used as the input for analyzing association rules, mining sequential patterns, recognizing clusters, and so on. However, discovering frequent patterns in large scale datasets is an extremely time consuming task. Most research in the area of association rule discovery has focused on the method of efficient frequent pattern discovery, e.g. Park, Chen & Yu (1995); Savasere, Omiecinski & Navathe (1995); Han, Pei & Yin (2000); Pei, Han, Lu, Nishio, Tang & Yang (2001). When seeking all associations that satisfy constraints on support and confidence, once frequent patterns have been identified, generating the association rules is trivial. In the last decade, various algorithms have been proposed on this problem, e.g. maximal pattern mining - Grahne & Zhu (2003); closed pattern mining - Pei, Han & Mao (2000); Grahne & Zhu (2003); mining the most interesting frequent patterns - Fu, Kwong & Tang (2000); Han, Wang, Lu & Tzvetkov (2002, 2005); Hirate, Iwahashi & Yamana (2004). However, some challenges are still existed and need to be overcome. In this paper, a mathematical space will be introduced with some new related concepts and propositions to design a new algorithm for solving frequent patterns mining problem. It is hoped that such an improved algorithm will be simple to implement and more efficient.

Clinical research has led to the development of new medications and medication strategy may change to gain better treatment outcomes. With the increasing attention to the evidence based medical guideline, it is important for clinicians to study the medication strategy changes. This study is interested in evaluating long-term prescriptions of type 2 diabetes patients provided by Kyoto University Hospital following the release of a new medication in 2010. Frequent sequential pattern mining (FSPM) is a prominent tools for extracting frequent patterns. However, the number of the result set could be enormous and may inhibit clinicians in assessing the results. To help the clinicians, a medication progression graph (MPG) is constructed using adjacent (1-sequence) frequent patterns produced by the singleton pattern mining. Compared to conventional frequent patterns, singleton's patterns features full itemsets and 1-step distance. Hence, the pattern represents the true medication transition event. Using the graph, a clinical physician was able to observe a significant increase of physician's activities in changing the medication after the release (i.e., in MPG that start with Sulfonylurea after 2010, the number of edges is increase by 2.83 and the number of nodes is increase by 2.27 compared to before 2010). Our preliminary results show that the new visualization method enables a clinician to analyze a medication strategy changes after the new medication released.

Internet of Things (IoT)-backed smart shopping carts are generating an extensive amount of data in shopping markets around the world. This data can be cleaned and utilized for setting business goals and strategies. Artificial intelligence (AI) methods are used to efficiently extract meaningful patterns or insights from such huge amounts of data or big data. One such technique is Association Rule Mining (ARM) which is used to extract strategic information from the data. The crucial step in ARM is Frequent Itemsets Mining (FIM) followed by association rule generation. The FIM process starts by tuning the support threshold parameter from the user to produce the number of required frequent patterns. To perform the FIM process, the user applies hit and trial methods to rerun the aforesaid routine in order to receive the required number of patterns. The research community has shifted its focus towards the development of top-K most frequent patterns not using the support threshold parameter tuned by the user. Top-K most frequent patterns mining is considered a harder task than user-tuned support-threshold-based FIM. One of the reasons why top-K most frequent patterns mining techniques are computationally intensive is the fact that they produce a large number of candidate itemsets. These methods also do not use any explicit pruning mechanism apart from the internally auto-maintained support threshold parameter. Therefore, we propose an efficient TKIFIs Miner algorithm that uses depth-first search strategy for top-K identical frequent patterns mining. The TKIFIs Miner uses specialized one- and two-itemsets-based pruning techniques for topmost patterns mining. Comparative analysis is performed on special benchmark datasets, for example, Retail with 16,469 items, T40I10D100K and T10I4D100K with 1000 items each, etc. The evaluation results have proven that the TKIFIs Miner is at the top of the line, compared to recently available topmost patterns mining methods not using the support threshold parameter.

Preparing a data set for analysis is generally the most time consuming task in a data mining project, requiring many complex SQL queries, joining tables and aggregating columns. Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instead of one number per row. This new class of functions is called horizontal aggregations. Horizontal aggregations build data sets with a horizontal denormalized layout (e.g. point-dimension, observation-variable, instance-feature), which is the standard layout required by most data mining algorithms. We propose three fundamental methods to evaluate horizontal aggregations: CASE: Exploiting the programming CASE construct; SPJ: Based on standard relational algebra operators (SPJ queries); PIVOT: Using the PIVOT operator, which is offered by some DBMSs. Experiments with large tables compare the proposed query evaluation methods. Our CASE method has similar speed to the PIVOT operator and it is much faster than the SPJ method. In general, the CASE and PIVOT methods exhibit linear scalability, where as the SPJ method does not. In a relational database, especially with normalized tables, a significant effort is required to prepare a summary data set that can be used as input for a data mining or statistical algorithm. Most algorithms require as input a data set with a horizontal layout, with several Records and one variable or dimension per column. That is the case with models like clustering, classification, regression and PCA; consult. Each research discipline uses different terminology to describe the data set. In data mining the common terms are point-dimension. Statistics literature generally uses observation-variable. Machine learning research uses instance-feature. This article introduces a new class of aggregate functions that can be used to build data sets in a horizontal layout (denormalized with aggregations), automating SQL query writing and extending SQL capabilities. We show evaluating horizontal aggregations is a challenging and interesting problem and we introduced alternative methods and optimizations for their efficient evaluation. II. MOTIVATION As mentioned above, building a suitable data set for data mining purposes is a time- consuming task. This task generally requires writing long SQL statements or customizing SQL Code if it is automatically generated by some tool. There are two main ingredients in such SQL code: joins and aggregations; we focus on the second one. The most widely- known aggregation is the sum of a column over groups of rows. Some other aggregations return the average, maximum, minimum or row count over groups of rows. There exist many aggregations functions and operators in SQL. Unfortunately, all these aggregations have limitations to build data sets for data mining purposes. The main reason is that, in general, data sets that are stored in a relational database (or a data warehouse) come

In order to solve the problem that urban spatial pattern causes by gradually deteriorating the urban microclimate,directly affecting the comfort of the living environment and to clarify the influence of the urban spatial configuration on residential microclimate, a microclimate detection system based on wireless sensor network is proposed to detect the microclimate of urban residential area,and a mathematical modeling method of the relationship between different parameters such as: urban spatial pattern parameters (building density, average height of building, horizontal layout and vertical layout), residential microclimate parameters (mean radiant temperature(MRT), and Physiological Equivalent Temperature (PET)) based on surrogate model is investigated to analyze the correlation between spatial pattern change of urban residential area and microclimate. The Kriging model and response surface model are used to establish the mathematical model between the spatial shape and the microclimate. The results showed that the kriging model has higher precision than the response surface model, so the kriging model was used to model the relationship between urban spatial model parameters and residential microclimate parameters. The results of the influence of spatial morphology parameters on microclimate indicate that the building density and average height of building are positively correlated with the PET and MRT parameters,and horizontal layout and vertical layout are negatively correlated with the PET and MRT parameters. Thus through reasonable building layout, we can optimize and improve the microclimate environment of residential area, which is helpful in guiding the design of building scheme.

BackgroundMany studies report about detection and functional characterization of cis-regulatory motifs in untranslated regions (UTRs) of mRNAs but little is known about the nature and functional role of their distribution. To address this issue we have developed a computational approach based on the use of data mining techniques. The idea is that of mining frequent combinations of translation regulatory motifs, since their significant co-occurrences could reveal functional relationships important for the post-transcriptional control of gene expression. The experimentation has been focused on targeted mitochondrial transcripts to elucidate the role of translational control in mitochondrial biogenesis and function.ResultsThe analysis is based on a two-stepped procedure using a sequential pattern mining algorithm. The first step searches for frequent patterns (FPs) of motifs without taking into account their spatial displacement. In the second step, frequent sequential patterns (FSPs) of spaced motifs are generated by taking into account the conservation of spacers between each ordered pair of co-occurring motifs. The algorithm makes no assumption on the relation among motifs and on the number of motifs involved in a pattern. Different FSPs can be found depending on different combinations of two parameters, i.e. the threshold of the minimum percentage of sequences supporting the pattern, and the granularity of spacer discretization. Results can be retrieved at the UTRminer web site: . The discovered FPs of motifs amount to 216 in the overall dataset and to 140 in the human subset. For each FP, the system provides information on the discovered FSPs, if any. A variety of search options help users in browsing the web resource. The list of sequence IDs supporting each pattern can be used for the retrieval of information from the UTRminer database.ConclusionComputational prediction of structural properties of regulatory sequences is not trivial. The presented data mining approach is able to overcome some limits observed in other competitive tools. Preliminary results on UTR sequences from nuclear transcripts targeting mitochondria are promising and lead us to be confident on the effectiveness of the approach for future developments.

Recent research in frequent pattern mining (FPM) has shifted from obtaining the complete set of frequent patterns to generating only a representative (summary) subset of frequent patterns. Most of the existing approaches to this problem adopt a two-step solution; in the first step, they obtain all the frequent patterns, and in the second step, some form of clustering is used to obtain the summary pattern set. However, the twostep method is inefficient and sometimes infeasible since the first step itself may fail to finish in a reasonable amount of time. In this paper, we propose an alternative approach to mining frequent pattern representatives based on a uniform sampling of the output space. Our new algorithm, Musk, obtains representative patterns by sampling uniformly from the pool of all frequent maximal patterns; uniformity is achieved by a variant of Markov Chain Monte Carlo (MCMC) algorithm. Musk simulates a random walk on the frequent pattern partial order graph with a prescribed transition probability matrix, whose values are computed locally during the simulation. In the stationary distribution of the random walk, all maximal frequent pattern nodes in the partial order graph are sampled uniformly. Experiments on various kind of graph and itemset databases validate the effectiveness of our approach.

As a branch of frequent pattern mining, the task-oriented pattern mining has received increasing attention due to its broad application scenarios. The lexicographic subset tree based algorithm and the multiobjective evolutionary algorithm are two effective approaches for finding the most frequent and complete pattern in task-oriented applications. However, both suffer from heavy computational cost since their runtime increases rapidly as the transaction dataset is scaled up. To address this issue, this paper regards the task-oriented pattern mining as a data-driven optimization problem and solves it by using a surrogate-assisted multiobjective evolutionary algorithm. Based on the framework of our previous multiobjective evolutionary algorithm for task-oriented pattern mining, the proposed algorithm estimates the objective values of most solutions using an ensemble of surrogates instead of the real objective functions, thereby highly improving the efficiency of the algorithm. Experimental results on three task-oriented applications indicate that the proposed algorithm has better efficiency than state-of-the-art algorithms.

Recommender systems are chiefly renowned for their applicability in e-commerce sites and social media. For system optimization, this work introduces a method of behaviour pattern mining to analyze the person's mental stability. With the utilization of the sequential pattern mining algorithm, efficient extraction of frequent patterns from the database is achieved. A candidate sub-sequence generation-and-test method is adopted in conventional sequential mining algorithms like the Generalized Sequential Pattern Algorithm (GSP). However, since this approach will yield a huge candidate set, it is not ideal when a large amount of data is involved from the social media analysis. Since the data is composed of numerous features, all of which may not have any relation with one another, the utilization of feature selection helps remove unrelated features from the data with minimal information loss. In this work, Frequent Pattern (FP) mining operations will employ the Systolic tree. The systolic tree-based reconfigurable architecture will offer various benefits such as high throughput as well as cost-effective performance. The database's frequently occurring item sets can be found by using the FP mining algorithms. Numerous research areas related to machine learning and data mining are fascinated by feature selection since it will enable the classifiers to be swift, more accurate, and cost-effective. Over the last ten years or so, there have been significant technological advancements in heuristic techniques. These techniques are beneficial because they improve the search procedure's efficiency, albeit at the potential sacrifice of completeness claims. A new recommender system for mental illness detection was based on features selected using River Formation Dynamics (RFD), Particle Swarm Optimization (PSO), and hybrid RFD-PSO algorithm is proposed in this paper. The experiments use the depressive patient datasets for evaluation, and the results demonstrate the improved performance of the proposed technique.

Existing studies for mining frequent XML query patterns mainly introduce a straightforward candidate generate-and-test strategy and compute frequencies of candidate query patterns from scratch periodically by checking the entire transaction database, which consists of XML query patterns transformed from user queries. However, it is nontrivial to maintain such discovered frequent patterns in real XML databases because there may incur frequent updates that may not only invalidate some existing frequent query patterns but also generate some new frequent ones. Accordingly, existing proposals are inefficient for the evolution of the transaction database. To address these problems, this paper presents an efficient algorithm IPS-FXQPMiner for mining frequent XML query patterns without candidate maintenance and costly tree-containment checking. We transform XML queries into sequences through a one- to-one mapping and then mine the frequent sequences to generate frequent XML query patterns. More importantly, based on IPS-FXQPMiner, an efficient incremental algorithm, Incre-FXQPMiner is proposed to incrementally mine frequent XML query patterns, which can minimize the I/O and computation requirements for handling incremental updates. Our experimental study on various real-life datasets demonstrates the efficiency and scalability of our algorithms over previous known alternatives.

The aim of this paper is focus discovering frequent patterns in Web log data and to obtain information about the navigational behavior of the users. Frequent pattern mining is researched area in the area of data mining. Using frequent pattern discovery methods in Web log data can result into good practices. Discovering hidden information from Web log data is called Web usage mining. The use of advertising purposes in creating dynamic user profiles etc. In this paper three pattern mining approaches are investigated from the Web usage mining point of view. The different patterns in Web log mining are page sets, page sequences and page graphs.

Similarity analysis of frequent sequential activity pattern mining