Unified Framework for Data Mining using Frequent Model Tree

Abstract: Data mining is the science of discovering hidden patterns from data. Over the past years, a plethora of data mining algorithms has been developed to carry out various data mining tasks such as classification, clustering, association mining and regression. All the methods are ad-hoc in nature, and there exists no unifying framework which unites all the data mining tasks. This study proposes such a framework which describes a data modelling technique to model data in a manner that can be used to accomplish all kinds of data mining tasks. This study proposed a novel algorithm known as Frequent Model (FM)-Growth, based on Frequent pattern (FP)-Growth algorithm. The algorithm is used to find frequent patterns or models from data. These models will then be used to carry out various data mining tasks such as classification, clustering. The advantage of these frequent models is that they can be used as it is with any data mining task irrespective of the nature of the task. The algorithm is carried out in two stages. In the first stage, we grow the FM-tree from the data and in the second stage, we extract the frequent models from the FM-tree. The accuracy of the proposed algorithm is high. However, the algorithm is computationally expensive when searching for frequent models in high volume and high dimensional data. The reason of expensiveness is that it needs to travel all the nodes of a tree. The study suggests measures to be taken to improve the efficiency of the overall process using dictionary data structure.Keywords: Data Mining, Frequent Pattern Recognition Unified Framework, Classification, Clustering, FPGrowth tree

Web Mining for Social Network Analysis:A Review, Direction and Future Vision.

Although web is rich in data, gathering this data and making sense of this data is extremely difficult due to its unorganised nature. Therefore existing Data Mining techniques can be applied toextract information from the web data. The knowledge thus extracted can also be used for Analysis of Social Networks and Online Communities. This paper gives a brief insight to Web Mining and Link Analysis used in Social Network Analysis and reveals the algorithms such as HITS, PAGERANK, SALSA, PHITS, CLEVER and INDEGREE which gives a measure to identify Online Communities over Social Networks. The most common amongst these algorithms are PageRank and HITS. PageRank measures the importance of a page efficiently with the help of inlinks in less time, while HITS uses both inlinks and outlinks to measure the importance of a web page and is sensitive to user query. Further various extensions to these algorithms also exist to refine the query based search results. It opens many doors for future researches to find undiscovered knowledge of existing online communities over various social networks.Keywords:Web Structure Mining, Link Analysis, Link Mining, Online Community Minin

Tri-level Unified Framework for Human Gait Analysis

There are several applications that can be related to multimedia content analysis. Considering video as one of the prominent forms of multimedia content, this paper presents analysis of human walking motion (gait) found in video sequences by using promising strategy of integrating techniques from data fusion and computer vision. To provide solutions to the challenges in human gait analysis a unified framework is proposed comprising of three different levels: data level, feature descriptor level and decision level. The three levels perform specific tasks assigned to them. At the data level, features are extracted from input video sequences for minimal representation. At the feature descriptor level, features from minimal representation are rearranged to build a feature descriptor and finally at decision level meaningful interpretations are performed. For analysing human walking motion found in video sequences, initially, moving silhouettes are extracted using background subtraction for minimal representation at the data level. The extracted silhouettes are then represented in a common representation in a spatial form followed by correlation analysis and a feature descriptor is developed with minimum interest points at the feature descriptor level. Finally, interpretation of normal gait poses and transition poses are made at the decision level.Keywords:Multimedia content; Data Fusion; Unified Framework; Background Subtraction;Correlation; Feature Descriptor; interpretation of Gaits

Energy optimization methods for Virtual Machine Placement in Cloud Data Center

The Information Technology industry has been upheaved by the influx of cloud computing. The extension of Cloud computing has resulted in the creation of huge data centers globally containing numbers of computers that consume large amounts of energy resulting in high operating costs. To reduce energy consumption providers must optimize resource usage by performing dynamic consolidation of virtual machines (VMs) in an efficient way. The problems of VM consolidation are host overload detection, host under-load detection, VM selection and VM placement. Each of the aforestated sub-problems must operate in an optimized manner to maintain the energy usage and performance. The process of VM placement has been focused in this work, and energy efficient, optimal virtual machine placement (E2OVMP) algorithm has been proposed. This minimizes the expenses for hosting virtual machines in a cloud provider environment in two different plans such as i) reservation and ii) on-demand plans, under future demand and price uncertainty. It also reduces energy consumption. E2OVMP algorithm makes a decision based on the gilt-edged solution of stochastic integer programming to lease resources from cloud IaaS providers. The performance of E2OVMP is evaluated by using CloudSim with inputs of planet lab workload. It minimized the user’s budget, number of VM migration resulting efficient energy consumption. It ensures a high level of constancy to the Service Level Agreements (SLA).Keywords: Cloud resource management; virtualization; dynamic consolidation; stochastic integer programming (SIP)*Cite as: Esha Barlaskar, N. Ajith Singh, Y. Jayanta Singh, “Energy optimization methods for Virtual Machine Placementin Cloud Data Center†ADBU J.Engg.Tech., 1(2014) 0011401(7pp

Enhanced Cuckoo Search Algorithm for Virtual Machine Placement in Cloud Data Centers

In order to enhance resource utilisation and power efficiency in cloud data centres it is important to perform Virtual Machine (VM) placement in an optimal manner. VM placement uses the method of mapping virtual machines to physical machines (PM). Cloud computing researchers have recently introduced various meta-heuristic algorithms for VM placement considering the optimised energy consumption. However, these algorithms do not meet the optimal energy consumption requirements. This paper proposes an Enhanced Cuckoo Search (ECS) algorithm to address the issues with VM placement focusing on the energy consumption. The performance of the proposed algorithm is evaluated using three different workloads in CloudSim tool. The evaluation process includes comparison of the proposed algorithm against the existing Genetic Algorithm (GA), Optimised Firefly Search (OFS) algorithm, and Ant Colony (AC) algorithm. The comparision results illustrate that the proposed ECS algorithm consumes less energy than the participant algorithms while maintaining a steady performance for SLA and VM migration. The ECS algorithm consumes around 25% less energy than GA, 27% less than OFS, and 26% less than AC

Modelling Objects Using Kernel Principal Component Analysis

Object detection is a technologically challenging and practically useful field of computer vision.The success of object detection relies on modelling of an object class. Statistical shape modelling is one of the popular method. Object modelling starts with asset of examples shapes (the training set), and learn from this the pattern of variability of the shape of the class of objects for which the training set can be considered a representative sample. Modelling can considered as the process of modelling the distribution of the training points in shape space. In this paper we present Kernel principal component analysis (KPCA) based active shape models (ASM) for learning the intra –class deformation modes of an object. KPCA is the non-linear dimensionality reduction method. The comparison on performance and space of KPCA and principal component analysis (PCA) are shownKeywords: Object model, KPCA, PCA, ASM.Cite as: Rajkumari Bidyalakshmi Devi, Romesh Laishram, Y.J. Singh, “Modelling Objects Using KernelPrincipal Component Analysis†ADBU J.Engg.Tech.,2(1)(2015) 0021102(5pp

Suggestive Local Engine for SQL Developer: SLED

Information Technology (IT) industry recruits junior staff on regular basis. Most of the applications use databases to store or access the data. Structure Query Language (SQL) is used to communicate with database middleware. An expensive SQL statement may engage the data centers for longer time forcing the organizations to sellout high cost for data storage and maintenance. A tool is required for training the junior developers. This study proposes a Suggestive Local Engine for SQL Developer (SLED). It develops a warehouse using the optimized SQL statements collected from reputed software firms or expert team. This study uses the concept of data marts to grouped the data and frequent pattern search algorithm to calculate frequencies and support of patterns of SQLstatements. This system suggests the developers based on the common patterns of SQL statements used by those experts. It also warns the developers if their writing pattern maps to the outlier statement. This system helps all the junior developers in an organization and graduates in colleges or universities to practice with suggestions.Keywords: Suggestive engine optimized SQL, Data Warehous