The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The collaborative information in horizontal collaborative fuzzy clustering is transmitted by partition matrix, which requires that the dimensions of collaborating partition matrix and collaborated partition matrix must be the same. It requires that the collaborative datasets are clustered into the same number of clusters, but in many cases it is not suitable or difficult to do. In this paper, a new...
The advances in location-acquisition technologies have generated massive spatio-temporal trajectory data, which represent the mobility of a diversity of moving objects over time, such as people, vehicles, and animals. Discovery of traveling companions on trajectory data has many real-world applications. Most of existing discovery approaches are limited to centralized computing, while these techniques...
In order to improve primary energy utilization, achieve economical operation of distribution network, comprehensively consider the concentration / compensation needs of various groups under typical load levels, and to gain understanding of characteristics of different types of user loads, the present paper proposes a hierarchical cluster algorithm to enhance the cohesion of a distribution feeder load...
Interval data is often met with in the real world. Fuzzy c-means for interval dataset (IFCM), an effective tool for clustering small or moderate scale interval dataset, can not deal with large scale interval dataset. This paper presents a new clustering method for this problem. The new method introduces collaborative mechanism into the IFCM to raise the efficiency. In other words, this method implements...
Network protocol classification plays an important role in modern network security and fine-grained management architectures. The state-of-the-art network protocol classification methods aim to take the advantages of flow statistical features and machine learning techniques. However the classification performance is severely affected by limited supervised information and unknown network protocols...
With the growing volume of the airport passengers, public transit is needed for healthy and sustainable city development, in which airport shuttle buses play a key role in satisfying the demand. In this paper, a two-phase airport shuttle bus stop planning method is proposed based on taxi GPS data. It aims at providing convenient public transit to the airport by identifying optimal airport shuttle...
In social media analysis, one critical task is detecting burst of topics or buzz, which is reflected by extremely frequent mentions of certain key words in a short time interval. Detecting buzz not only provides useful insights into the information propagation mechanism, but also plays an essential role in preventing malicious rumors. However, buzz modeling is a challenging task because a buzz time-series...
Clustering, as a part of the Data Mining field, has been in the center of the research attention for the last decade. It is the task of finding subsets of data that are sharing the same type of attributes. Text Clustering becomes one of the most critical and important solutions in data mining to discover knowledge from fast grow up web data and log files. There are many challenges, algorithms needs...
Data analytics involves choosing between many different algorithms and experimenting with possible combinations of those algorithms. Existing approaches however do not support scientists with the laborious tasks of exploring the design space of computational experiments. We have developed a framework to assist scientists with data analysis tasks in particular machine learning and data mining. It takes...
To solve the class imbalance problem in classification of pre-miRNAs with ab initio method, a novel sample selection method is proposed according to the characteristics of pre-miRNAs. Real/pseudo pre-miRNAs are clustered based on their stem similarity and their distribution in high dimensional sample space respectively. The training samples are selected according to the sample density of each cluster...
The investigation of community structures in networks is an important issue in many domains and disciplines. However, Most of the present algorithms consider only structure of the network, ignoring some additional conditions such as direction, weight, semantic, etc. In this paper the behaviors of each vertex are focus. Based on the previous work, two limitations of swarm similarity in closely community...
From the functional point of view, a method that employed an Immune Programming algorithm to extract rules from Neural Network (NN) was presented. According to quantifying color feature of landscape images had been marked with emotion values in China Affective Picture System (CAPS), the method clustered outputs of hidden neurons of NN, reduced the searching scale, and the mapping between color and...
System anomaly detection is very important for development, maintenance and performance refinement in large scale distributed systems. It's a good way to obtain the troubleshooting and problem diagnosis by analyzing system logs produced by distributed systems. However, due to the increasing scale and complexity of distributed systems, the size of logs must be very large. Thus, it's inefficient for...
In this paper, we proposed an advanced face analysis platform for large-scale consumer photos, namely PFAP. Leveraging Client/Server architecture, the platform provides users high-performance face clustering and near-real time image retrieval service. Advanced face analysis schema, two-level parallel computing architecture and analysis as a service are three key innovations in PFAP. In face analysis...
Emails play an important role in our daily life. It has been recognized that clustering emails into meaningful groups can greatly save cognitive load to process emails. Mailbox user becomes more and more concerned about how to organize and manage the emails as well as how to mine the meaningful data conveniently and effectively. This paper proposes a novel personal topics detection approach using...
The investigation of community structures in networks is an important issue in many domains and disciplines.Closely communicating community is different from the traditional community which emphasize particularly on structure or context. The definition of closely communicating community and measuring method are introduced firstly. Based on the previous work, the closely communicating community detection...
Actual demand and cost in logistics system changed with time. The facility location problem considering the time factor was studied in this paper. A dynamic logistics nodes location model considering capacitated, multi-source and multi-level logistics nodes was presented. We also considered the influence of the transition between hub and non-hub logistics nodes, and established a new node. Based on...
Traditional topic tracking approaches can obtain the relevant stories. However, the relationship between stories occurred during the topic developing process can not be exhibited clearly. By analyzing the evolution of the topic, the concept subtopic is put forward and the focus of topic evolution analysis is subtopic instead. Four levels topic model is constructed. The subtopic detection algorithm,...
In the area of topic tracking, topic is developing with time. Actually speaking, traditional topic tracking approaches could track the relevant stories. However, the relation between events occurred during the topic development process could not be learned with traditional approaches. Furthermore, the whole history of the topic tracking could not be acquired either. Based on these disadvantages in...
Rare category detection is the task of identifying examples from rare classes in an unlabeled data set. It is an open challenge in machine learning and plays key roles in real applications such as financial fraud detection, network intrusion detection, astronomy, spam image detection, etc. In this paper, we develop a new graph-based method for rare category detection named GRADE. It makes use of the...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.