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Louvain algorithm is known to be fast to deliver good results in detecting communities of big graphs. This paper further speeds up Louvain algorithm by restricting the internal search rules and early pruning the non-promising candidates. Experimental results of the modified algorithm on various sized data show outstanding speedups of up to 40.5 and 4.7 times for weighted graphs and unweighted graphs,...
Discovery of useful patterns from human movement behavior can convey valuable knowledge to a variety of critical applications. Existing approaches focus on outdoor group discovery and mainly consider objects who belong to the same cluster as a possible group, which leads to the inability to discover all the existing groups. This is especially true for indoor human-generated trajectories, where spatially...
Bursty behavior normally indicates that the workload generated by data accesses happens in short time, uneven spurts. In order to handle the bursts, the physical resources of IT devices have to be configured to offer capability which goes far beyond the average resource utilization, thus satisfying the performance. However, this kind of fat provisioning incurs wasting resources when the system does...
Recommendation systems have become extremely common in recent years, and are utilized in a variety of areas to predict the "rating" or "preference" that a user would give to a point of interest (PoI), such as a restaurant, a hotel, or a bar. Such systems typically produce a list of recommendations by considering previous ratings of the user, as well as ratings of other users. Not...
There is an increasing need to quickly understand the contents log data. A wide range of patterns can be computed and provide valuable information: for example existence of repeated sequences of events or periodic behaviors. However patternminingtechniquesoftenproducemanypatternsthathave to be examined one by one, which is time consuming for experts. On the other hand, visualization techniques are...
Sequential pattern mining from spatiotemporal data has received much attention in recent years due to its broad application domains such as targeted advertising, location prediction for taxi services, and urban planning. The characteristics of spatiotemporal sequences vary widely depending on the discovered knowledge type. Most of the recent approaches focus on the point-based spatiotemporal data...
In the last years a new right-wing, populist and eurosceptic party emerged in Germany, the 'Alternative für Deutschland'. Topics that were used by the party to draw attention to their program included the Euro-crisis and the so-called 'refugee crisis'. We investigate some aspects of social media use of the AfD. Our goal is to relate the rise of this party to some quantitative measures of their social...
Incorporating accurate prognostic information into clinical decision making could advance evidence-based, person-centered healthcare by more effectively targeting healthcare services to those patients most likely to benefit. Here we describe the deployment of predictive models for five year life expectancy of patients, built on electronic health records (EHR) of nearly 7,500 patients aged 50 and above,...
While state-of-the-art kernels for graphs with discrete labels scale well to graphs with thousands of nodes, the few existing kernels for graphs with continuous attributes, unfortunately, do not scale well. To overcome this limitation, we present hash graph kernels, a general framework to derive kernels for graphs with continuous attributes from discrete ones. The idea is to iteratively turn continuous...
Joint clustering of multiple networks has been shown to be more accurate than performing clustering on individual networks separately. Many multi-view and multi-domain network clustering methods have been developed for joint multi-network clustering. These methods typically assume there is a common clustering structure shared by all networks, and different networks can provide complementary information...
In this paper, we attempt to analyze the relationships between the stay time of outpatients and treatment processes they received based on the temporal pattern mining algorithm proposed by Batal et al. We could observe MPTPs (Minimal Predictive Temporal Patterns) of treatment processes containing 'co-occur' relations as well as injections in the classes where the patients spent long time in receiving...
With the rapid development of Internet technology and smart devices, tremendous amounts of multimedia data (e.g. text, image, video, audio, etc.) are produced and uploaded online every day. Semi-supervised learning has been proved to be one effect and effective solution to manage the massive emerging multimedia, which usually leverages the performance by exploiting the local geometry of a small number...
Graph-OLAP is an online analytical framework which allows us to obtain various projections of a graph, each of which helps us view the graph along multiple dimensions and multiple levels. Given a series of snapshots of a temporal heterogeneous graph, we aim to find interesting projections of the graph which have anomalous evolutionary behavior. Detecting anomalous projections in a series of such snapshots...
The success of any Information Retrieval system relies upon extracting relevant pages of similar knowledge matching the requirements of the user. The traditional best of all statistical methodologies fails in conquering the issues of relevancy and redundancy of web pages retrieved. In this paper we propose a novel architecture, FP Growth based Fuzzy Particle swarm optimization which captures the dynamicity...
To increase the learning effectiveness and willingness of students became the most important issue for the Universities in Taiwan. Therefore, we must find the important factors of the learning effectiveness to improve the learn willingness of students. However, it is not easy to measure the learning effectiveness because the subjective judgment of evaluators and the attributes of factors are always...
Mozambique has been affected by multiple conflicts since colonial rule. This paper proposes an e-PAZ Early Warning System that helps in identifying potential conflicts. The system filters conflict related news from social media. It also offers geographic and socioeconomic information of the conflict zone. It provides qualitative and quantitative analysis on the past conflicts and gives user an open...
Frequent itemset mining is one of the most common of data mining tasks. In its simplest form, one is given a table of data in which the columns represent attributes and each row specifies a value for each attribute, each attribute-value pair being referred to as an item. The task is to find sets of these items that occur frequently in the data, where frequency is specified as a minimum occurrence...
The possibility of extracting useful medical information from data collected by nurses for management purposes is investigated. An alternating decision tree for predicting pressure ulcer development is generated from nursing needs score data (NNS) usually recorded in Japanese hospitals.
Public business information could increase the efficiency and fairness of the securities market for the benefit of investors, corporations, and economy. Since 1934, the U. S. Securities and Exchange Commission (SEC) has required disclosure of all listed companies in forms and documents. SEC's EDGAR, which is a data management system of SEC, began to collect electronic documents to help investors get...
Stacking occurs in the composite creation of documents (e.g., such as academic papers, presentation slides, and notes) and when they are refined in research activities. It is important but actually difficult for researchers to satisfactorily accumulate the deliverables for each work and their circumstances so that they can skillfully conduct later work. This problem becomes particularly serious for...
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