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Social networking sites, like Twitter, have become the fastest means of communication. Millions of user tweet everyday discussing recent and important issues. Generally, the tweets are identical or similar in nature, which causes information overload on user's wall. This makes it difficult for the user to keep a track of all the events. The best solution for this is to summarize tweets that are similar,...
Class imbalance of instances is a common problem in the field of data mining and machine learning. A dataset is considered to be imbalanced if one of the classes (further called a minority class or positive class) contains much smaller number of instances than the remaining classes (majority classes or negative class). We describe a new approach to balance data with improved classification. Resampling...
Cluster structure is one of the most important structures that have received increasing attention over recent years. This paper presents a modification to the structure of the cluster tree Wireless Sensor Network (WSN) dividing clusters into sub-groups. Each group of these sub-groups operates like a tree from a small number of nodes. The results from the implemented adjustments indicate a significant...
The paper presents the researches to determine the effectiveness of different criteria to estimate the complex biology objects clustering quality. The gene expression sequences of cancer patients were used as experimental data. The degree of the studied objects similarity was estimated by the comparison of the gene expression sequences profile using different metrics to estimate the objects proximity...
Fault localization is very important to both researchers and practitioners. Running tests is a useful approach to identify the fault location. Researchers have studied how to automatically identify faults in database applications [1], [2], [3]. However, those research considers the entire SQL statement as one line of code, indicating that the whole SQL statement contains errors. Little attention has...
In recent times enumerable number of clustering algorithms have been developed whose main function is to make sets of objects having almost the same features. But due to the presence of categorical data values, these algorithms face a challenge in their implementation. Also some algorithms which are able to take care of categorical data are not able to process uncertainty in the values and so have...
We demonstrate a personalized user authentication mechanism based on first-person-view videos. Our proposed algorithm forms temporary image-based authentication challenges which benefit a variety of purposes such as unlocking a mobile device or fallback authentication. First, representative frames are extracted from the egocentric videos. Then, they are split into distinguishable segments before repetitive...
Internet of things (IoT) integrate the technologies such as sensing, communication, networking and cloud computing in wide range monitoring zone. For applications of IoT, the most appropriate monitoring network is wireless sensor networks (WSN). It is most important to develop energy efficient cluster head (CH) selection scheme to increase the network lifetime of WSNs. It is most crucial to save the...
Game theory is a powerful tool that has recently been used in networks to improve the end users' quality of experience (e.g. decreased response time, higher delivery rate). In this paper, we propose to use game theory in the context of Content Delivery Networks (CDNs) to organize video contents into clusters having similar request profiles. The popularity of each content in the cluster can be determined...
Coordinated Multi-Point (CoMP) systems appear as advanced promising strategies to improve user throughputs, especially in interference limited regions, at cell edge. Whether CoMP consists in jointly processing data from multiple transmission points, or in smartly coordinating the allocation of resources, CoMP implementation requires significant computation effort, signaling exchange that may impact...
The technique of collaborative filtering in recommender system suffers from data sparsity and cold start. In this paper, a cluster based approach is proposed for alleviating the problem of sparsity by transferring the knowledge from a more densely rated concomitant domain. The paper focuses on providing recommendation in a sparsely rated domain by transferring the knowledge from the highly rated domain...
ROCK is a popular algorithm to cluster categorical data due to its ingenious concept of links between them. The only issue with this method is time complexity. The procedure is inherently slow with maximum iterations N-k. This paper shows how properties of dataset can be utilized to reduce the total iterations by a factor of 10 or more. The reduction is much significant as the size of dataset grows...
Dengue fever is one of the major health related issues as reported in World Health Organization (WHO). Therefore, a study is needed on the factors that influencing dengue incidences. This paper presents the influence of dengue incidence with dual climate variable in the 3D form scatter plot. Machine learning techniques such as clustering and regression is done to compare the sum square of residual...
Finding of frequent sub-graphs is an important operation on graphs and it is defined as detection of all sub-graphs that appear frequently in a set of graphs. This paper proposes detection of frequent sub-community graph from n-set of community graph of villages; are useful for characterizing community graph sets, finding difference among groups of community graphs, classifying and clustering of community...
The Levy Walk (or Levy flight) is a concept fromBiomathematics to describe the hunting–behaviour of manypredatory species. It is a very efficient way to find prey in avery short time frame. We now want to use this concept ina clustering–context to – if you so will – "hunt" for clusters. We describe how we convert this concept into an efficient wayto find cluster centres by linking the data...
Many kinds of real world data can be modeled by a heterogeneous information network (HIN) which consists of multiple types of objects. Clustering plays an important role in mining knowledge from HIN. Several HIN clustering algorithms have been proposed in recent years. However, these algorithms suffer from one or moreof the following problems: (1) inability to model general HINs, (2) inability to...
Domain adaptation has achieved promising results in many areas, such as image classification and object recognition. Although a lot of algorithms have been proposed to solve the task with different domain distributions, it remains a challenge for multi-source unsupervised domain adaptation. In addition, most of the existing algorithms learn a classifier on the source domain and predict the labels...
Borgs et al. [2016] investigated essential requirements for communities in preference networks. They defined six axioms on community functions, i.e., community detection rules. Though having elegant properties, the practicality of this axiomsystem is compromised by the intractability of checking twocritical axioms, so no nontrivial consistent community functionwas reported in [Borgs et al., 2016]...
Clustering vertices in graphs or in sequences of graphs has important applications in network science, bioinformatics, and other areas. Most research to date has focused on static graphs or sequences where the number of vertices does not change. We propose a new algorithm that successfully partitions the vertices of a graph sequence into smooth clusters, even when the number of vertices is allowed...
It seems necessary to detect a broken bike rooted at a station in near realtime as the number of bikes within bikeshare systems has reached more than a million in 2015. Indeed, a bike that cannot be moved is not cost effective in terms of number of trips. This brings frustration to users who were expecting to find a bike at that station without knowing that it is actually defective. We thus propose...
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