Distributed frequent hierarchical pattern mining for robust and efficient large-scale association discovery

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

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.

The critical node detection problems (CNDPs) have important applications in network security, smart grid, epidemic control, drug design, and risk assessment. The critical node problem (CNP) is one of well-known CNDPs, which is NP-hard. In this work, we combine frequent pattern mining with evolutionary algorithm for solving CNP, where pattern mined from high-quality solutions are used to guide the construction of offspring solution. More specifically, based on the memetic algorithm (Memetic Algorithm for CNP, MACNP), frequent pattern mining was integrated into the memetic algorithm framework. The frequent patterns mined were used to construct the offspring solution, instead of crossover operator in MACNP. In the research, the nodes in frequent items set were directly fixed as part of the offspring solution. So the offspring solution inherited excellent properties from more parent solutions, thus forming the algorithm called Frequent Pattern Based Search for CNP (FPBS-CNP). This strategy improved the algorithm efficiency. Experiments were conducted on synthetic and real-world benchmark. We experimentally compare FPBS-CNP with MACNP. The experiment results show that FPBS-CNP performs well on small instances and has potential in solving large instances.

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.

Traditional frequent pattern mining considers equal profit/weight value of every item. Weighted Frequent Pattern (WFP) mining becomes an important research issue in data mining and knowledge discovery by considering different weights for different items. Existing algorithms in this area are based on fixed weight. But in our real world scenarios the price/weight/importance of a pattern may vary frequently due to some unavoidable situations. Tracking these dynamic changes is very necessary in different application area such as retail market basket data analysis and web click stream management. In this paper, we propose a novel concept of dynamic weight and an algorithm DWFPM (dynamic weighted frequent pattern mining). Our algorithm can handle the situation where price/weight of a pattern may vary dynamically. It scans the database exactly once and also eligible for real time data processing. To our knowledge, this is the first research work to mine weighted frequent patterns using dynamic weights. Extensive performance analyses show that our algorithm is very efficient and scalable for WFP mining using dynamic weights.

Even though weighted frequent pattern (WFP) mining is more effective than traditional frequent pattern mining because it can consider different semantic significances (weights) of items, existing WFP algorithms assume that each item has a fixed weight. But in real world scenarios, the weight (price or significance) of an item can vary with time. Reflecting these changes in item weight is necessary in several mining applications, such as retail market data analysis and web click stream analysis. In this paper, we introduce the concept of a dynamic weight for each item, and propose an algorithm, DWFPM (dynamic weighted frequent pattern mining), that makes use of this concept. Our algorithm can address situations where the weight (price or significance) of an item varies dynamically. It exploits a pattern growth mining technique to avoid the level-wise candidate set generation-and-test methodology. Furthermore, it requires only one database scan, so it is eligible for use in stream data mining. An extensive performance analysis shows that our algorithm is efficient and scalable for WFP mining using dynamic weights.

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.

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.

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.

Abstract. Frequent sequential pattern mining in item interval extended sequence database (iSDB) has been one of interesting task in recent years. Unlike classic frequent sequential pattern mining, the pattern mining in iSDB also consider the item interval between successive items; thus, it may extract more meaningful sequential patterns in real life. Most previous frequent sequential pattern mining in iSDB algorithms needs a minimum support threshold (minsup) to perform the mining. However, it’s not easy for users to provide an appropriate threshold in practice. The too high minsup value will lead to missing valuable patterns, while the too low minsup value may generate too many useless patterns. To address this problem, we propose an algorithm: TopKWFP – Top-k weighted frequent sequential pattern mining in item interval extended sequence database. Our algorithm doesn’t need to provide a fixed minsup value, this minsup value will dynamically raise during the mining process

In this paper, we focus on frequent pattern mining using non Boolean similarity functions Several properties and propositions that allow pruning the search space of frequent similar patterns, are proposed Based on these properties, an algorithm for mining frequent similar patterns using non Boolean similarity functions is also introduced We evaluate the quality of the frequent similar patterns computed by our algorithm by means of a supervised classifier based on frequent patterns.

Similarity analysis of frequent sequential activity pattern mining

High volumes of wide varieties of valuable data of different veracities can be easily generated or collected at a high velocity from various big data applications and services. Embedded in these big data are valuable knowledge and useful information, which can be discovered by data science solutions. As a popular data science task, frequent pattern mining aims to discover implicit, previously unknown and potentially useful information and valuable knowledge in terms of sets of frequently co-occurring items. Many of the existing frequent pattern mining algorithms return large numbers of frequent patterns, of which only a small portion may be of interest to users. In this paper, we present a constrained mining algorithm that allows crowds of users to collaboratively vote for their interesting patterns. Such an algorithm takes the benefits of crowdsourcing, crowdvoting and collaborative filtering for the data analytics and mining of popular constrained frequent patterns from big data applications and services.

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.

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.