Mining time-dependent influential users in Facebook fans group

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

Social power (the ability to control or influence another’s thoughts, feelings, or behaviors) and empathy (the ability to both share and understand the thoughts and feelings of others) are fundamental to social life. Here, we explore the relationship between social power and the ERP components associated with empathy for pain. Participants were induced into states of high and low social power via a double blind version of the episodic recall task (e.g., “recall a time you felt powerful”). Afterward, they completed a pain categorization task, viewing pictures of hands that were in pain or not in pain, from a first-person or third-person visual perspective. Whereas both high and low social power states were associated with enhanced N2 amplitudes when observing another in pain, only the high social power state was associated with an enhancement of the P3. Based on this pattern of data, we tentatively suggest that, whereas social power does not seem to impact the initial emotional response to observing another’s pain (as indexed by the N2), low social power might induce changes in the cognitive evaluation of another’s pain relative to high social power (as indexed by the P3). We discuss our findings in relation to the broader literature on power and empathy.

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.

Analysis of descriptive epidemiological characteristics of pancreatic cancer in Vojvodina, Serbia. The study covers population of Vojvodina in the period from 2000 to 2009. The method used for data processing was the descriptive. The data, referring to a specified period of time, were analyzed from chronological and demographic aspects and according to histological diagnosis. In the period from 2000 to 2009, there were 2,108 registered cases of pancreatic cancer of which 1,886 had a fatal outcome. Standardized incidence rates varied between 5.7 and 9.1 per 100,000 population in males and between 4.2 and 5.3 in females. Linear incidence trends in males in the specified period of time, based on crude (r=0.7883, p<0.05) and standardized (r=0,6373, p<0,05) incidence rates, demonstrated increase. Annual percent increase in the crude incidence rate was 4.5% in males, and 2.8% in females. Age-standardized mortality rates varied between 5.2 and 7.5 per 100,000 population in males and 3.6 and 4.7 in females. Linear mortality trends in males in the specified period of time, based on crude (r=0.8795, p<0.05) and standardized (r=0.7669, p<0.05) mortality rates, also demonstrated annual percent increase. Data analysis shows unfavorable onco-epidemiological situation related to pancreatic cancer in Vojvodina, in aspects of both incidence and mortality. Absence of primary and secondary prevention does not allow medical institutions to successfully fight against this disease.

Social networks have become an important marketing tools for business to build brand pages to prompt their new products. Fans' user-experience diffusion results in great marketing power that people never seen. Typically, some of the fans in group are influence users. They are market movers which mean they can influence others buying decisions. Businesses can affect online influence users by giving them extra benefits to turn them into spokesmen. However, who is the influential user? What period of time is appropriate for information to spread? In this study, a framework based on frequent pattern mining is proposed to find the influence users as well as the proper time to spread information. The one day 24-hour period can be divided into successive time segments. An influence transaction that contains fans' influence power will be defined in each time segment. After transactions being collected several days, the frequent patterns can be found to deduce the proper time for influence users to spread information. The theoretical experiment is given to show how the proposed framework works.

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).

There is intense interest in understanding the stochastic and dynamical properties of the global Foreign Exchange (FX) market, whose daily transactions exceed one trillion US dollars. This is a formidable task since the FX market is characterized by a web of fluctuating exchange rates, with subtle inter-dependencies which may change in time. In practice, traders talk of particular currencies being 'in play' during a particular period of time -- yet there is no established machinery for detecting such important information. Here we apply the construction of Minimum Spanning Trees (MSTs) to the FX market, and show that the MST can capture important features of the global FX dynamics. Moreover, we show that the MST can help identify momentarily dominant and dependent currencies.

Deviation/waiver

Keynes' General Theory of Employment, Interest and Money and Sraffa's Production of Commodities by Means of Commodities were concerned with different problems, and this is reflected in the structure of the models they used. Keynes was concerned with the factors determining the level of employment in a particular period of time — a short period — and the ways in which that level of employment could be thanged over a sequence of these short periods. There is great scope for disappointment of expectations and uncertainty in his analysis — it is not tied in any way to long-period equilibrium. Sraffa's analysis was concerned with highly abstract theoretical questions — the value nature of capital and the lack of logical foundations for any marginal productivity theory of distribution that treats aggregate capital as an input. For this purpose, an analysis solely concerned with long-period equilibrium situations is not inappropriate. In these works there is no meeting ground between Sraffa and Keynes. The joining of the two analyses — except as a very special case in which entrepreneurial investment-expectations turned out to be validated by events — would distort seriously the nature of Keynes' analysis.

Pediatric health services research has documented that uninsured children are less likely to receive needed medical, mental health, and dental services and are more likely to experience adverse health outcomes.1 Uninsured children can be defined in 1 of the following 3 ways: 1) lacking insurance during a specified period of time, usually the most recent calendar year or past 12 months; 2) lacking insurance at any time during a specified period of time; or 3) lacking insurance at the specific point in time when a survey is done. During the first 3 years of life about one quarter of our nation’s children have at least 1 month when they are uninsured and over one half of these children remain uninsured for >6 months.2 The rate of uninsured children is constantly changing with the state of the economy, increases in employer insurance premiums, and changes in access to public insurance programs. Current economic conditions suggest that the number of uninsured children is rising from ∼8.5 million reported from 1998 to 2000 to perhaps more than the 11 million reported for 1997.3 However, there is a great deal of confusion associated with estimating the number of uninsured children because of differences among surveys with respect to when and how often they are administered, numbers of survey respondents and their geographic distribution, insurance definitions, age classifications, and other measurement issues.4 Using the 1999 National Survey of America’s Families survey, the Urban Institute estimated that among the country’s 72 million children, 51.2% were eligible for a publicly financed health plan, 33.4% were eligible for Medicaid, and 17.8% were eligible for the State Children’s Health Insurance Program (SCHIP).5 During this time period only 20.4% of children were enrolled in Medicaid and SCHIP based on Current Population Survey survey data. This … Address correspondence to Stephen Berman, MD, Children’s Hospital, 1056 E 19th Ave, B032, Denver, CO 80218. E-mail: berman.stephen{at}tchden.org

A Gibbs sampling scheme to the product partition model: an application to change-point problems

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.

The equation of exchange (often referred to as the quantity equation) is one of the oldest formal relationships in economics, early versions of both verbal and algebraic forms appearing at least in the 17th century. Perhaps the best known variant of the equation of exchange is that expressed by Irving Fisher (1922):$$ MV=PT $$Equation 1 represents a simple accounting identity for a money economy. It relates the circular flow of money in a given economy over a specified period of time to the circular flow of goods. The left-hand side of Eq. 1 stands for money exchanged, the right-hand side represents the goods, services and securities exchanged for money during a specified period of time. M is defined as the total quantity of money in the economy, T as the total physical volume of transactions, where a transaction is defined as any exchange of goods, including physical capital, services and securities for money, P is an appropriate price index representing a weighted average of the prices of all transactions in the economy. Finally, to make the stock of money comparable with the flow of the value of transactions (PT), and to make the two sides of the equation balance, it is multiplied by V, the transactions velocity of circulation, defined as the average number of times a unit of currency turns over (or changes hands) in the course of effecting a given year’s transactions.KeywordsMoney SupplyMoney DemandQuantity TheoryLegal TenderNominal IncomeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

How much Klout do you have … A test of system generated cues on source credibility

This paper proposes a simulink model of wind turbine using induction generator and performance analysis of high voltage direct current power converter station. This paper also shows how wind power can easily be generated with help of induction generator. The need of developing such a system promoted the utilization of nonconventional energy resources like wind power. For a developing country like India the electrical energy much mire required to achieve the estimated targets within the specified period of time. Since the power generation is not only our concern but also power transmission have great importance to transport the electrical energy over long distance easily and efficienctly, which ensures uninterrupted and regulated power supply at load end. Hence this paper also proposes a model to transmit electrical power in DC form. Since it is difficult to load long extra high voltage (EHV) ac lines to their thermal limits as a sufficient margin is kept against transient instability. With the model proposed in this thesis, it will be possible to load these lines close to their thermal limits. The transmission lines are allowed to carry usual ac along with dc superimposed on it.