Rare Items Research Articles

Popularity-based scalable peer-to-peer topology growth

Peer-to-peer (P2P) networks have gained importance and spread significantly during the last decades resulting in raised research interest in this area. The basic premise of P2P design is higher scalability and many existing large-scale applications, such as Twitter and Skype, use a form of P2P design. Although structured P2P designs enable one to guarantee finding of every item (rare or popular), they do not scale beyond a point and support from servers are needed. This breaks the decentralized design of the P2P system and results in a hybrid scheme. Unstructured P2P networks, on the other hand, can scale to much larger nodes but yet cannot give time guarantee for finding a rare item.Topological characteristics of unstructured P2P networks have impact on the efficiency of a search for items. Earlier studies have shown that popularity of the nodes has significant impact in the self-organization of the overlay topology. It is well known that unstructured P2P designs are very good when searching for a popular item. But, when a new trend is emerging, it is a rare item until it becomes highly popular. During that transitional period, unstructured designs suffer from inefficiency in finding to-be-popular (TBP) items. Such TBP items could be stuck at nodes with small degrees and thus become hard to find. We hypothesize that if the overlay topology is established and grown with a more proactive consideration of the items’ popularity, the delay in finding TBP items could be reduced significantly. Further, such topology growth will reduce the search time for popular items. Thus, the overall search performance of the P2P system will significantly improve as well, since most of the searches are for popular items. In this paper, we investigate incorporating item popularity into the overlay topology in a scalable way.

A mockingbird Mimus polyglottos (Linnaeus, 1758) found in a stomach of a blue shark Prionace glauca (Linnaeus, 1758).

A cenzontle Mimus polyglottos was found in the stomach of a juvenile Prionace blue shark glauca caught off the coast of Magdalena Bay, Baja California Sur, Mexico. This continental bird is a rare item like shark prey. This note helps to emphasize the role of Prionace glauca as a opportunistic predator and / or scavenger.

A MANUFACTURING FAILURE ROOT CAUSE ANALYSIS IN IMBALANCE DATA SET USING PCA WEIGHTED ASSOCIATION RULE MINING

Root cause analysis is key issue for manufacturing processes. It has been a very challenging problem due to the increasing level of complexity and huge number of operational aspects in manufacturing systems. Association rule mining (ARM) which aids in root cause analysis was introduced to extract interesting correlations, frequent patterns, associations or casual structures among items in the transactional database. Although ARM was proven outstanding in many application domains, not many researches were focusing on solving rare items problem in imbalance dataset. The existence of imbalanced dataset in manufacturing environment make the classical ARM fails to extract interesting pattern in an efficient way. Weighted association rule mining (WARM) overcomes the rare items problem by assigning weights to items. The goal of using weighted support is to make use of the weight in the mining process and priorities the selection of the selection of targeted itemsets according to their significance, rather than frequency alone. However, the development of a suitable weight assignment scheme has been an important issue. In this research, we proposed principal component analysis (PCA) to automate the weight in WARM. The result shows that PCA-WARM is capable in capturing pattern from the data of industrial process. These patterns are proven able to explain industrial failure.

Efficient constraint-based Sequential Pattern Mining (SPM) algorithm to understand customers’ buying behaviour from time stamp-based sequence dataset

AbstractBusiness Strategies are formulated based on an understanding of customer needs. This requires development of a strategy to understand customer behaviour and buying patterns, both current and future. This involves understanding, first how an organization currently understands customer needs and second predicting future trends to drive growth. This article focuses on purchase trend of customer, where timing of purchase is more important than association of item to be purchased, and which can be found out with Sequential Pattern Mining (SPM) methods. Conventional SPM algorithms worked purely on frequency identifying patterns that were more frequent but suffering from challenges like generation of huge number of uninteresting patterns, lack of user’s interested patterns, rare item problem, etc. Article attempts a solution through development of a SPM algorithm based on various constraints like Gap, Compactness, Item, Recency, Profitability and Length along with Frequency constraint. Incorporation of s...

MMORPG에서 GFG 쇠퇴를 위한 현금거래 구매자 탐지 방안에 관한 연구

As the rise in popularity of online games, the users start exchanging rare items for real money. As RMT (Real Money Trade) is prevalent, GFG (Gold Farming Group) who abuse RMT shows up. GFG causes social problems such as identity theft, privacy leaks. Because they needs many bot characters to gather game items. In addition, GFG induce RMT that makes in-game problems such as a destroying game economy, account hacking. Therefore, It is very important work to collapse GFG at the perspective of social and in-game. In this paper, we proposed a fundamental method for detecting RMT buyers for the collapse of GFG at the perspective of buyer by Law of Demand and Supply. We found two type of RMT by analyzing actual game data and detected RMT buyers with high recall ratio of 98% by ruled-based detection.

MMORPG 게임 내 계정도용 탐지 모델에 관한 연구

As game item trading becomes more popular with the rapid growth of online game market, the market for trading game items by cash has increased up to KRW 1.6 trillion. Thanks to this active market, it has been easy to turn these items and game money into real money. As a result, some malicious users have often attempted to steal other players` rare and valuable game items by using their account. Therefore, this study proposes a detection model through analysis on these account thieves` behavior in the Massive Multiuser Online Role Playing Game(MMORPG). In case of online game identity theft, the thieves engage in economic activities only with a goal of stealing game items and game money. In this pattern are found particular sequences such as item production, item sales and acquisition of game money. Based on this pattern, this study proposes a detection model. This detection model-based classification revealed 86 percent of accuracy. In addition, trading patterns when online game identity was stolen were analyzed in this study.

An Enhanced Method to Mine Rare Item Sets using Multiple Item Sets Support based on CP-Tree

An Enhanced Method to Mine Rare Item Sets using Multiple Item Sets Support based on CP-Tree

A new tree-based approach for evaluating rule antecedent constraint in confabulation based association rule mining

Confabulation-based Association Rule Mining (CARM) is an algorithm that is inspired by the thought process of human brain. It is an iterative method where, in each iteration, new rules are generated using a measure of cogency and constraints on the antecedents and consequents of the previous iteration. This cogency measure leads to a particular ability in dealing with rare items problem. Here, we aim to study CARM's antecedent constraint with respect to the ability of pruning uninteresting rules. Hence, a new Tree-based CARM (TCARM) algorithm is proposed which produces all rules using only cogency constraint on the consequents. In our experiments, the extracted rules of these two algorithms are compared with those of CFPGrowth using several measures such as precision, recall, support and cogency of rules. CFPGrowth is an approach that finds association rules using multiple minimum support and confidence, so the extracted rules of CFPGrowth support rare items and can be used for evaluating the extracted rules of CARM and TCARM. Our analysis based on the proposed TCARM and the extracted rules of CFPGrowth, on both synthetic and real datasets, show that CARM prunes uninteresting rules better than TCARM, which confirms that its antecedent constraint can help to control the number of extracted rules properly.

User Impact on Selection, Digitization, and the Development of Digital Special Collections

Libraries and archives digitize their special collections in an effort to increase access to rare and unique items. To ensure that resulting digital collections meet user needs, institutions have made assessment, consultation, and user participation integral to digitization initiatives and the selection process. Institutions must also build digital collections that reflect their priorities. This article examines user impact on selection and the process of developing useful and usable digital special collections that balance user and institutional interests while satisfying the needs of key stakeholders.

A Novel Approach for Finding Rare Items Based on Multiple Minimum Support Framework

Abstract Pattern mining methods describe valuable and advantageous items from a large amount of records stored in the corporate datasets and repositories. While mining, literature has almost singularly focused on frequent itemset but in many applications rare ones are of higher interest. For Example medical dataset can be considered, where rare combination of prodrome plays a vital role for the physicians. As rare items contain worthwhile information, researchers are making efforts to examine effective methodologies to extract the same. In this paper, an effort is made to analyze the complete set of rare items for finding almost all possible rare association rules from the dataset. The Proposed approach makes use of Maximum constraint model for extracting the rare items. A new approach is efficient to mine rare association rules which can be defined as rules containing the rare items. Based on the study of relevant data structures of the mining space, this approach utilizes a tree structure to ascertain the rare items. Finally, it is demonstrated that this new approach is more virtuous and robust than the existing algorithms.

A Recent Overview: Rare Association Rule Mining

association rules are mine useful information form large dataset. Traditional association mining methods generate frequent rules based on frequent itemsets with reference of minimum support and minimum confidence threshold which specified by user. It called as support-confidence framework. As many of generated rules are of no use, further analysis is essential to find interesting Rules. A rule that contains rare items can consider as rare association rule. Rare Association Rules Represent unpredictable or unknown association, so it is more interesting than frequent association rule. Rare association rule mining provides relationship between items which occurs uncommonly. This paper presents brief survey in the area of rare association rule mining. Keywordspattern, support, confidence, Rare Items

A novel approach for mining cyclically repeated patterns with multiple minimum supports

Periodic patterns and cyclic patterns have been used to discover recurring patterns in sequence databases. Toroslu (2003) proposed cyclically repeated pattern (CRP) mining, in which a new parameter called repetition support is considered in the mining process. In a data sequence, the occurrence of a subsequence must satisfy a single user-specified minimum repetition support. However, in real-life applications, items may occur at various frequencies in a database. The rare item problem may occur when all items are set to a single minimum repetition support. To solve this problem, we included the concept of multiple minimum supports to enable users to specify the multiple minimum item repetition support (MIR) according to the natures of items. In this paper, we first redefined CRPs based on the MIR and original form of the sequence minimum support. A new algorithm, rep-PrefixSpan, was developed for discovering a complete set of CRPs in sequence databases. The experimental results indicate that the proposed approach exhibits performance superior to that of conventional CRP mining. The proposed method can be applied in many application domains including customer purchase behavior, web logging, and stock analyses.

London pride in context

The treasures volume, showcasing rare, beautiful, or significant items, is proliferating in 21stcentury Britain. This is despite the alternative of the World Wide Web to provide a cheaper, quicker, easier way for libraries to publicize their choicest holdings. But what is the treasures volume really doing, and how indicative are its contents of our most valuable items? This article is based upon the author’s experience of selecting 60 items for the 2012 treasures volume, Senate House Library, University of London. It describes the rationale behind the choices and contextualizes within the genre in the United Kingdom more widely, looking at other treasures volumes and at web displays, to generalize about libraries’ presentations of themselves. It notes emphasis on unique, distinctive, and relevant items and on institutional identity, and interprets the modern treasures volume as a political document to justify libraries’ existence by highlighting their distinguishing features.

A Backward Glance over Some Leading Citizens in the Poe World

Having grown up, academically speaking, in the shadow of Arthur Hobson Quinn, who lived roughly half an hour from my college, Ursinus, and with so many of my college teachers having studied their American literature at the University of Pennsylvania, where Quinn was supreme when American literature was still a fairly new subject in university instruction, and now with myself looking like I might well be a contemporary of Quinn, or even Poe and John Greenleaf Whittier, and being from a family in which genealogy was important, it may be natural that I should represent historical memory and share my recollections of some persons who pioneered in and opened up Poe studies. Some may be surprised to learn that Professor Quinn was, first and foremost, not a Poe specialist, but that his preeminent scholarly love was American drama. For pre-twentieth-century American plays, Quinn’s studies typically remain, after nearly a century, almost the only informed commentaries. Many have become increasingly aware that William Dunlap, America’s first major playwright, and the novelist Charles Brockden Brown, might each properly be deemed the Adam to American literary Gothicism. To return to Professor Quinn: when I began my own academic studies, he was in his eighties, ill, and housebound. He spent most of his time nestled in a chair, swathed in blankets and quilts, and wearing a sun shade, claiming that reading so many early American plays had ruined his eyesight. Before such ills befell him, Quinn was active in having established the Clothier Collection of American Drama at the University of Pennsylvania, which remains one of the foremost collections of early American plays. The Poe interest emerged from Quinn’s own work in American drama—Poe’s parents being actors—and from his aim to furnish an accurate biographical portraiture of Poe which would demolish the long-standing depiction by Rufus Griswold. Spending more than twenty years in preparing that biography, which appeared first in 1941 but has stood the test of time well enough to go through several reprintings, Quinn achieved a solid narrative account, though many of his critical opinions have been modified or superseded in the work of others. A Quinn student, J. Albert Robbins, followed his mentor’s practices in determining to present factually accurate scholarship, as some early volumes in the American Literary Scholarship journal attest. Robbins often recounted to me anecdotes of his days as Quinn’s student. Those who know Quinn primarily for

Discovering high utility itemsets with multiple minimum supports

Generally, association rule mining uses only a single minimum support threshold for the whole database. This model implicitly assumes that all items in the database have the same nature. In real applications, however, each item can have different nature such as medical datasets which contain information of both diseases and symptoms or status related to the diseases. Therefore, association rule mining needs to consider multiple minimum supports. Association rule mining with multiple minimum supports discovers all item rules by reflecting their characteristics. Although this model can identify meaningful association rules including rare item rules, not only the importance of items such as fatality rate of diseases but also attribute of items such as duration of symptoms are not considered since it treats each item with equal importance and represents the occurrences of items in transactions as binary values. In this paper, we propose a novel tree structure, called MHU-Tree (Multiple item supports with High Utility Tree), which is constructed with a single scan. Moreover, we propose an algorithm, named MHU-Growth (Multiple item supports with High Utility Growth), for mining high utility itemsets with multiple minimum supports. Experimental results show that MHU-Growth outperforms the previous algorithm on both real and synthetic datasets, and can discover useful rules from a medical dataset.

Infrequent Weighted Item Set Mining in Complex Data Analysis

Infrequent Weighted Association Mining (IWAM) is one of the main areas in data mining for extracting the rare items in high dimensional datasets. Traditional Association rule mining algorithms produce large number of candidate sets along with the database scans. Due to large number of transactions and database size, traditional methods consume more time to find the relevant association rules with the specified threshold. Prior and post database scans are required an additional effort to validate the association rules. Most of the existing weighted models are implemented for mining frequent itemsets, but finding infrequent itemset mining are useful in many recent fields like web,medical,cloud,complex databases,protein sequence etc. In weighted infrequent association rule mining, each item in the transaction is assigned a weight in order to mine high utility infrequent itemsets. In this proposed work, weighted association rule mining algorithm is proposed to find infrequent itemsets using weighted threshold measures. Proposed approach gives better results on real-time datasets compare to existing weighted models.

Information weighted sampling for detecting rare items in finite populations with a focus on security

Frequently one has to search within a finite population for a single particular individual or item with a rare characteristic. Whether an item possesses the characteristic can only be determined by close inspection. The availability of additional information about the items in the population opens the way to a more effective search strategy than just random sampling or complete inspection of the population. We will assume that the available information allows for the assignment to all items within the population of a prior probability on whether or not it possesses the rare characteristic. This is consistent with the practice of using profiling to select high risk items for inspection. The objective is to find the specific item with the minimum number of inspections. We will determine the optimal search strategies for several models according to the average number of inspections needed to find the specific item. Using these respective optimal strategies we show that we can order the numbers of inspections needed for the different models partially with respect to the usual stochastic ordering. This entails also a partial ordering of the averages of the number of inspections. Finally, the use, some discussion, extensions, and examples of these results, and conclusions about them are presented.

Deep secrets of a sun and surf resort in the Atlantic

The majority of igneous rocks can be classified into either the basalt–gabbro group, or the rhyolite–granite group. However, there also exist a wide range of rocks whose composition lies outside these broad groupings, some of which are compositionally extreme and contain many rare minerals, sometimes as essential components. Petrologists have always interested themselves in these unusual rock types, just as collectors always value rare items over the commonplace. Many of these unusual rock types have been given weird and wonderful names, driving non‐petrologists to distraction, although a more scientific system of nomenclature has yet to receive general acclaim. The giving of a plethora of names to igneous rocks was largely a phenomenon of the early years of petrology in the late nineteenth and early twentieth century and many will be surprised to learn that new names are still being coined as with the, as yet unauthorized, bermudite, not surprisingly, called after the island of Bermuda.

Efficient discovery of correlated patterns using multiple minimum all-confidence thresholds

Correlated patterns are an important class of regularities that exist in a database. Although there exists no universally acceptable best measure to judge the interestingness of a pattern, all-confidence is emerging as a popular measure to discover the patterns. It is because the measure satisfies both the anti-monotonic and null-invariance properties. The former property makes the pattern mining practicable in real-world applications. The latter property facilitates the user to discover the patterns involving both frequent and rare items without generating the huge number of patterns. In this paper, we show that though the measure satisfies the null-invariance property, mining the patterns containing both frequent and rare items with a single minimum all-confidence (minAllConf) threshold leads to the dilemma known as "rare item problem." At a high minAllConf, the discovered correlated patterns involving rare items have very short length. At a low minAllConf, combinatorial explosion can occur, producing too many patterns. To confront the problem, the paper introduces an alternative model based on the concept of multiple minAllConf thresholds. The proposed model generalizes the existing model of correlated patterns and facilitates the user to specify a different minAllConf for each pattern depending upon its items' frequencies. A pattern-growth algorithm, called GCoMine, has also been proposed to discover the patterns. Experiment results show that GCoMine is efficient, and the proposed model can address the problem effectively.

다중 최소 임계치 기반 빈발 패턴 마이닝의 성능분석

Dept. of Computer Engineering, Sejong University, Seoul, 143-74 7, Korea* Corresponding author (yunei@sejong.ac.kr)[Received 5 August 2013, Reviewed 9 August 2013, Accepted 10 October 2013]☆ 본 논문은 2013년도 정부 교육과학기술부의 재원으로 한국 연구재단(NRF)의 지원을 받아 수행된 연구사업임 (No.2013005682 and 20080062611).☆본 논문은 2013년도 인터넷정보학회 춘계학술발표대회 우수논문 추천에 따라 확장 및 수정된 논문임.