Using Multi-Core HW/SW Co-design Architecture for Accelerating K-means Clustering Algorithm

The capability of classifying and clustering a desired set of data is an essential part of building knowledge from data. However, as the size and dimensionality of input data increases, the run-time for such clustering algorithms is expected to grow superlinearly, making it a big challenge when dealing with BigData. K-mean clustering is an essential tool for many big data applications including data mining, predictive analysis, forecasting studies, and machine learning. However, due to large size (volume) of Big-Data, and large dimensionality of its data points, even the application of a simple k-mean clustering may become extremely time and resource demanding. Specially when it is necessary to have a fast and modular dataset analysis flow. In this paper, we demonstrate that using a two-level filtering algorithm based on binary kd-tree structure is able to decrease the time of convergence in K-means algorithm for large datasets. The two-level filtering algorithm based on binary kd-tree structure evolves the SW to naturally divide the classification into smaller data sets, based on the number of available cores and size of logic available in a target FPGA. The empirical result on this two-level structure over multi-core FPGA-based architecture provides 330X speed-up compared to a conventional software-only solution

FI-GRL: Fast Inductive Graph Representation Learning via Projection-Cost Preservation

Graph representation learning aims at transforming graph data into meaningful low-dimensional vectors to facilitate the employment of machine learning and data mining algorithms designed for general data. Most current graph representation learning approaches are transductive, which means that they require all the nodes in the graph are known when learning graph representations and these approaches cannot naturally generalize to unseen nodes. In this paper, we present a Fast Inductive Graph Representation Learning framework (FI-GRL) to learn nodes' low-dimensional representations. Our approach can obtain accurate representations for seen nodes with provable theoretical guarantees and can easily generalize to unseen nodes. Specifically, in order to explicitly decouple nodes' relations expressed by the graph, we transform nodes into a randomized subspace spanned by a random projection matrix. This stage is guaranteed to preserve the projection-cost of the normalized random walk matrix which is highly related to the normalized cut of the graph. Then feature extraction is achieved by conducting singular value decomposition on the obtained matrix sketch. By leveraging the property of projection-cost preservation on the matrix sketch, the obtained representation result is nearly optimal. To deal with unseen nodes, we utilize folding-in technique to learn their meaningful representations. Empirically, when the amount of seen nodes are larger than that of unseen nodes, FI-GRL always achieves excellent results. Our algorithm is fast, simple to implement and theoretically guaranteed. Extensive experiments on real datasets demonstrate the superiority of our algorithm on both efficacy and efficiency over both macroscopic level (clustering) and microscopic level (structural hole detection) applications.Comment: ICDM 2018, Full Versio

An intelligent recommendation system framework for student relationship management

In order to enhance student satisfaction, many services have been provided in order to meet student needs. A recommendation system is a significant service which can be used to assist students in several ways. This paper proposes a conceptual framework of an Intelligent Recommendation System in order to support Student Relationship Management (SRM) for a Thai private university. This article proposed the system architecture of an Intelligent Recommendation System (IRS) which aims to assist students to choose an appropriate course for their studies. Moreover, this study intends to compare different data mining techniques in various recommendation systems and to determine appropriate algorithms for the proposed electronic Intelligent Recommendation System (IRS). The IRS also aims to support Student Relationship Management (SRM) in the university. The IRS has been designed using data mining and artificial intelligent techniques such as clustering, association rule and classification

A Multi-Disciplinary Review of Knowledge Acquisition Methods: From Human to Autonomous Eliciting Agents

This paper offers a multi-disciplinary review of knowledge acquisition methods in human activity systems. The review captures the degree of involvement of various types of agencies in the knowledge acquisition process, and proposes a classification with three categories of methods: the human agent, the human-inspired agent, and the autonomous machine agent methods. In the first two categories, the acquisition of knowledge is seen as a cognitive task analysis exercise, while in the third category knowledge acquisition is treated as an autonomous knowledge-discovery endeavour. The motivation for this classification stems from the continuous change over time of the structure, meaning and purpose of human activity systems, which are seen as the factor that fuelled researchers' and practitioners' efforts in knowledge acquisition for more than a century. We show through this review that the KA field is increasingly active due to the higher and higher pace of change in human activity, and conclude by discussing the emergence of a fourth category of knowledge acquisition methods, which are based on red-teaming and co-evolution

A Data as a Service (DaaS) Model for GPU-based Data Analytics

Cloud-based services with resources to be provisioned for consumers are increasingly the norm, especially with respect to Big data, spatiotemporal data mining and application services that impose a user's agreed Quality of Service (QoS) rules or Service Level Agreement (SLA). Considering the pervasive nature of data centers and cloud system, there is a need for a real-time analytics of the systems considering cost, utility and energy. This work presents an overlay model of GPU system for Data As A Service (DaaS) to give a real-time data analysis of network data, customers, investors and users' data from the datacenters or cloud system. Using a modeled layer to define a learning protocol and system, we give a custom, profitable system for DaaS on GPU. The GPU-enabled pre-processing and initial operations of the clustering model analysis is promising as shown in the results. We examine the model on real-world data sets to model a big data set or spatiotemporal data mining services. We also produce results of our model with clustering, neural networks' Self-organizing feature maps (SOFM or SOM) to produce a distribution of the clustering for DaaS model. The experimental results thus far show a promising model that could enhance SLA and or QoS based DaaS.Comment: Accepted, 23 December 2017, by the IEEE IFIP NTMS Workshop on Big Data and Emerging Trends WBD-ET 2018; it was later withdrawn because of funding issues. An extended/enhanced version will be published in future dates in related journal

The Survey of Data Mining Applications And Feature Scope

In this paper we have focused a variety of techniques, approaches and different areas of the research which are helpful and marked as the important field of data mining Technologies. As we are aware that many Multinational companies and large organizations are operated in different places of the different countries.Each place of operation may generate large volumes of data. Corporate decision makers require access from all such sources and take strategic decisions.The data warehouse is used in the significant business value by improving the effectiveness of managerial decision-making. In an uncertain and highly competitive business environment, the value of strategic information systems such as these are easily recognized however in todays business environment,efficiency or speed is not the only key for competitiveness.This type of huge amount of data are available in the form of tera-topeta-bytes which has drastically changed in the areas of science and engineering.To analyze,manage and make a decision of such type of huge amount of data we need techniques called the data mining which will transforming in many fields.This paper imparts more number of applications of the data mining and also focuses scope of the data mining which will helpful in the further research.Comment: International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.3, June 2012, 16 pages, 1 tabl

Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation

Remote sensing (RS) image retrieval is of great significant for geological information mining. Over the past two decades, a large amount of research on this task has been carried out, which mainly focuses on the following three core issues: feature extraction, similarity metric and relevance feedback. Due to the complexity and multiformity of ground objects in high-resolution remote sensing (HRRS) images, there is still room for improvement in the current retrieval approaches. In this paper, we analyze the three core issues of RS image retrieval and provide a comprehensive review on existing methods. Furthermore, for the goal to advance the state-of-the-art in HRRS image retrieval, we focus on the feature extraction issue and delve how to use powerful deep representations to address this task. We conduct systematic investigation on evaluating correlative factors that may affect the performance of deep features. By optimizing each factor, we acquire remarkable retrieval results on publicly available HRRS datasets. Finally, we explain the experimental phenomenon in detail and draw conclusions according to our analysis. Our work can serve as a guiding role for the research of content-based RS image retrieval

Grid-based Approaches for Distributed Data Mining Applications

The data mining field is an important source of large-scale applications and datasets which are getting more and more common. In this paper, we present grid-based approaches for two basic data mining applications, and a performance evaluation on an experimental grid environment that provides interesting monitoring capabilities and configuration tools. We propose a new distributed clustering approach and a distributed frequent itemsets generation well-adapted for grid environments. Performance evaluation is done using the Condor system and its workflow manager DAGMan. We also compare this performance analysis to a simple analytical model to evaluate the overheads related to the workflow engine and the underlying grid system. This will specifically show that realistic performance expectations are currently difficult to achieve on the grid

Survey of data mining approaches to user modeling for adaptive hypermedia

The ability of an adaptive hypermedia system to create tailored environments depends mainly on the amount and accuracy of information stored in each user model. Some of the difficulties that user modeling faces are the amount of data available to create user models, the adequacy of the data, the noise within that data, and the necessity of capturing the imprecise nature of human behavior. Data mining and machine learning techniques have the ability to handle large amounts of data and to process uncertainty. These characteristics make these techniques suitable for automatic generation of user models that simulate human decision making. This paper surveys different data mining techniques that can be used to efficiently and accurately capture user behavior. The paper also presents guidelines that show which techniques may be used more efficiently according to the task implemented by the applicatio