Using Non Boolean Similarity Functions for Frequent Similar Pattern Mining

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.

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.

Most of the existing algorithms for mining frequent patterns could produce lots of projected databases and short patterns which could increase the time and memory cost of mining. In order to overcome such shortcoming, a fast and efficient algorithm named FBPM for mining frequent patterns in biological sequence is proposed. We first present the concept of primary pattern, and then use prefix tree for mining frequent primary patterns. A pattern growth approach is also presented to mine all the frequent patterns without producing large amount of irrelevant patterns. Our experimental results show that FBPM not only improves the performance but also achieves effective mining results.

The behavior of the users of a website may change so quickly that it becomes a real challenge to attempt to make predictions according to the frequent patterns coming from the analysis of an access log file. In order to reduce the obsolescence of behavioral patterns as much as possible, the ideal method would provide frequent patterns in real time, making the result immediately available. In this paper, we propose a method for finding frequent behavioral patterns in real time, whatever the number of connected users. Considering how fast frequent behavior patterns may have changed since the time the access log file was analyzed, this result thus provides completely appropriate navigation schemata for predicting user behavior. Based on a distributed heuristic, our method also tackles and provides answers to several problems within the framework of data mining: the discovery of ‘interesting zones’ (a large number of frequent patterns concentrated over a period of time, or ‘super-frequent’ patterns), discovering very long sequential patterns and interactive data mining (‘on-the-fly’ modification of the minimum support).

Finding frequent subgraph patterns in uncertain graph databases become an important problem with applications. The main difficulty in solving the problem is finding frequent subgraph patterns from the large number of candidate graph and testing large number of subgraph isomorphism from the graphs that contain a given pattern. The traditional method of finding frequent subgraph patterns cannot distinguish the diverse patterns of uncertain graph database. In this paper, we propose a method named mining uncertain and diverse subgraph pattern (MUDSP) in PPI networks. It is used to find the different subgraph pattern including high proportion or high probability pattern. The algorithm is also enables necessary optimisations with respect to pruning index tree. The evaluation of our approach on real-world datasets of uncertain graph databases demonstrates the efficiency with respect to approach of finding diverse pattern.

Mining frequent inter-transaction patterns (ITPs) from large databases is both useful and of interest. Since frequent inter-transaction patterns (FITPs) are discovered across transactions in a transaction database (TD), the number of patterns is very large. Therefore, the mining time and memory usage are very high. Although several algorithms have been proposed for mining FITPs, they still require long runtime and high memory usage. Recent research shows that N-list-based approaches are very efficient for mining frequent patterns (FPs). Therefore, in this paper, we propose an N-list-based algorithm, called NL-ITP-Miner, to mine FITPs. In the proposed algorithm, we adopt the advantages of the N-list structure to build up the IT-PPC-tree. During the process of building the IT-PPC-tree, NL-ITP-Miner applies our proposed theorems to eliminate infrequent inter-transaction 1-patterns to reduce the search space. NL-ITP-Miner scans the database once to find frequent inter-transaction (FIT) 1-patterns for constructing the IT-PPC-tree, after that, the NL-ITP-Miner algorithm traverses this tree to generate frequent 1-patterns, FIT 1-patterns with their respective N-lists. Besides, we also propose effective pruning strategies that help NL-ITP-Miner to reduce the search space significantly and generate FITPs more quickly. Experiments show that NL-ITP-Miner outperforms the state-of-the-art algorithms for mining FITPs in terms of runtime and memory usage.

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.

Tick populations and tick‐borne diseases like Lyme borreliosis have been steadily increasing since the mid‐1990s. Realizing the threat that ticks pose to public health, two Dutch citizen science projects have collected tick bite reports since 2006. This unique volunteered geographical dataset, which currently has nearly 35,000 reports, was used to identify environmental and other circumstantial factors associated with tick bites. For this, we first enriched the tick bite reports with temperature, precipitation, vegetation and volunteered data associated with the location of the tick bite. Using this enriched dataset, we then derived a series of features to characterize the environmental and volunteer‐related conditions in which each tick bite occurred. Next, we discretized these features using the Jenks Natural Breaks algorithm and, after that, we mined frequent environmental patterns associated with tick bites using the AprioriClose algorithm. Finally, we checked that these patterns are specifically associated with the tick bites by comparing them with the frequent patterns mined from pseudo‐random locations. The frequent patterns were visualized using heat maps and ring maps and two representative patterns associated with tick bites were projected into geographic space to study their spatio‐temporal distribution. Our results show that factors linked to human activity are more relevant to model tick bites than seasonal accumulations of temperature, vegetation or precipitation. In particular, the number of warm and dry days per season are present in a significant number of patterns and the majority of tick bites are produced within a distance of half a kilometer of a forest, recreational or built‐up area. The study of patterns in the time‐series revealed that there are several persistent patterns consistently occurring each year and the validation process showed that the volunteer tick bites collection is capturing environmental conditions associated with tick bites, suggesting that these reports have a high scientific value. These results support the creation of a Dutch tick bite risk map that, in turn, will open the door to the design of public health interventions to reduce the incidence of Lyme disease.

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.

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.

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.

Mining of frequent patterns is one of the popular knowledge discovery and data mining (KDD) tasks. It also plays an essential role in the mining of many other patterns such as correlation, sequences, and association rules. Hence, it has been the subject of numerous studies since its introduction. Most of these studies find all the frequent patterns from collection of precise data, in which the items within each datum or transaction are definitely known and precise. However, there are many real-life situations in which the user is interested in only some tiny portions of these frequent patterns. Finding all frequent patterns would then be redundant and waste lots of computation. This calls for constrained mining , which aims to find only those frequent patterns that are interesting to the user. Moreover, there are also many real-life situations in which the data are uncertain. This calls for uncertain data mining . In this article, we propose algorithms to efficiently find constrained frequent patterns from collections of uncertain data .

Similarity analysis of frequent sequential activity pattern mining

Most of the frequent pattern mining algorithms search for all frequent patterns. However, there are many real-life situations in which users are interested in only some tiny portions of the mined frequent patterns. For mining of constrained frequent patterns, several classes of user constraints---such as anti-monotone constraints---have been proposed and their properties have been exploited. In this paper, we introduce a new class of constraints called mixed monotone constraints. We exploit its property for effective mining of frequent patterns satisfying user constraints that sum both positive and negative numerical values.