Concept-Based Pages Recommendation by Using Cluster Algorithm

[[abstract]]In this research, we used a proxy server to search for information related to the userpsilas browsed Web pages. From the records of the proxy server we constructed a profile of the userpsilas browsing habits. At the end of the userpsilas search subsystem, we will use content based concept to extract keywords to obtain the articlepsilas characteristicspsila description. From the recommendation system, the Web pages will be classified using the hierarchical grouping method, and through collaborative filtering, the recommendation Web pages will be chosen to provide further readings for students language learning.[[conferencedate]]20080701~20080705[[iscallforpapers]]Y[[conferencelocation]]Santander, Cantabria, Spai

Audience Segmentation in Extension Horticultural Programs

Cluster analysis was used to segment horticulture clientele using data from a needs assessment. Gardeners were segmented into two groups based on their horticulture practices. These groups were described using several factors including age and time spent maintaining different garden areas. Results from this study indicate the importance of considering the target audience prior to design and implementation of a gardening certificate program

A binary level set method based on k-Means for contour tracking on skin cancer images

A great challenge of research and development activities have recently highlighted in segmenting of the skin cancer images. This paper presents a novel algorithm to improve the segmentation results of level set algorithm with skin cancer images. The major contribution of presented algorithm is to simplify skin cancer images for the computer aided object analysis without loss of significant information and to decrease the required computational cost. The presented algorithm uses k-means clustering technique and explores primitive segmentation to get initial label estimation for level set algorithm. The proposed segmentation method provides better segmentation results as compared to standard level set segmentation technique and modified fuzzy cmeans clustering technique

Understanding and Personalising Smart City Services Using Machine Learning, the Internet-of-Things and Big Data

This paper explores the potential of Machine Learning (ML) and Artificial Intelligence (AI) to lever Internet of Things (IoT) and Big Data in the development of personalised services in Smart Cities. We do this by studying the performance of four well-known ML classification algorithms (Bayes Network (BN), Naïve Bayesian (NB), J48, and Nearest Neighbour (NN)) in correlating the effects of weather data (especially rainfall and temperature) on short journeys made by cyclists in London. The performance of the algorithms was assessed in terms of accuracy, trustworthy and speed. The data sets were provided by Transport for London (TfL) and the UK MetOffice. We employed a random sample of some 1,800,000 instances, comprising six individual datasets, which we analysed on the WEKA platform. The results revealed that there were a high degree of correlations between weather-based attributes and the Big Data being analysed. Notable observations were that, on average, the decision tree J48 algorithm performed best in terms of accuracy while the kNN IBK algorithm was the fastest to build models. Finally we suggest IoT Smart City applications that may benefit from our work

An Effective Clustering Approach to Stock Market Prediction

In this paper, we propose an effective clustering method, HRK (Hierarchical agglomerative and Recursive K-means clustering), to predict the short-term stock price movements after the release of financial reports. The proposed method consists of three phases. First, we convert each financial report into a feature vector and use the hierarchical agglomerative clustering method to divide the converted feature vectors into clusters. Second, for each cluster, we recursively apply the K-means clustering method to partition each cluster into sub-clusters so that most feature vectors in each sub-cluster belong to the same class. Then, for each sub-cluster, we choose its centroid as the representative feature vector. Finally, we employ the representative feature vectors to predict the stock price movements. The experimental results show the proposed method outperforms SVM in terms of accuracy and average profits

Analisis Hubungan antara Massa, Tinggi, Body Mass Index, dan Umur Atlet terhadap Posisi dalam Basket Menggunakan K-Mean Clustering

Abstrak—NBA adalah salah satu ajang kompetisi olahraga dalam bidang permainan bola basket yang populer dan diikuti oleh tim dan pemain professional. Dalam olahraga basket terdapat pembangian posisi tertentu dalam permainan dan atlet akan ditempatkan sesuai dengan kemampuan yang dimilikinya. Oleh karena itu, kami melakukan penelitian untuk mencari tahu adakah hubungan tinggi, massa, umur dan body mass index dalam menentukan posisi pemain selain dilihat dari kemampuan pemainnya. Cara mencari hubungan ini dengan melakukan pengolaan data yang ada dan data tersebut dilakukan pengelompokkan menggunakan metode K-Mean Clustering. Hasil dari penelitian yang dilakukan terdapat hubungan antara tinggi, massa, dan BMI terhadap posisi pemain. Namun, umur tidak berpengaruh terhadap penentuan posisi pemain.Kata Kunci—NBA, BasketBall, K-Mean Clusterin

Intrusion detection system using hybrid GSA-k-Means

Security is an important aspect in our daily life. Intrusion Detection Systems (IDS) are developed to be the defense against security threats. Current signature based IDS like firewalls and antiviruses, which rely on labeled training data, generally cannot detect novel attacks. The purpose of this study is to improve the performance of IDS in terms of detection accuracy and reduce False Alarm Rate (FAR). Clustering is an important task in data mining that is used in IDS applications to detect novel attacks. Clustering refers to grouping together data objects so that objects within a cluster are similar to one another, while objects in different clusters are dissimilar. K-Means is a simple and efficient algorithm that is widely used for data clustering. However, its performance depends on the initial state of centroids and may trap in local optima. The Gravitational Search Algorithm (GSA) is one effective method for searching problem space to find a near optimal solution. In this study, a hybrid approach based on GSA and k-Means (GSA-kMeans), which uses the advantages of both algorithms, is presented. The performance of GSA-kMeans is compared with other well-known algorithms, including k-Means and Gravitational Search Algorithm (GSA). Experimental results on the KDDCup 1999 dataset have demonstrated that the proposed method is more efficient in the detection of intrusive behavior than conventional k-Means and standard GSA which shows 80.62% detection accuracy and 7.45% FAR

Metastatic triple negative breast cancer adapts its metabolism to destination tissues while retaining key metabolic signatures

Triple negative breast cancer (TNBC) metastases are assumed to exhibit similar functions in different organs as in the original primary tumor. However, studies of metastasis are often limited to a comparison of metastatic tumors with primary tumors of their origin, and little is known about the adaptation to the local environment of the metastatic sites. We therefore used transcriptomic data and metabolic network analyses to investigate whether metastatic tumors adapt their metabolism to the metastatic site and found that metastatic tumors adopt a metabolic signature with some similarity to primary tumors of their destinations. The extent of adaptation, however, varies across different organs, and metastatic tumors retain metabolic signatures associated with TNBC. Our findings suggest that a combination of anti-metastatic approaches and metabolic inhibitors selected specifically for different metastatic sites, rather than solely targeting TNBC primary tumors, may constitute a more effective treatment approach

Identifying and understanding road-constrained areas of interest (AOIs) through spatiotemporal taxi GPS data: A case study in New York City

Urban areas of interest (AOIs) represent areas within the urban environment featuring high levels of public interaction, with their understanding holding utility for a wide range of urban planning applications. Within this context, our study proposes a novel space-time analytical framework and implements it to the taxi GPS data for the extent of Manhattan, NYC to identify and describe 31 road-constrained AOIs in terms of their spatiotemporal distribution and contextual characteristics. Our analysis captures many important locations, including but not limited to primary transit hubs, famous cultural venues, open spaces, and some other tourist attractions, prominent landmarks, and commercial centres. Moreover, we respectively analyse these AOIs in terms of their dynamics and contexts by performing further clustering analysis, formulating five temporal clusters delineating the dynamic evolution of the AOIs and four contextual clusters representing their salient contextual characteristics