Data Mining

DATA MINING

Sai Info solution provide the Project Development & Training. We Develop Project for BE/ME/PHD.. Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems An interdisciplinary subfield of computer science, it is an essential process — wherein intelligent methods are applied to extract data patterns— the overall goal of which is to extract information from a data set, and transform it into an understandable structure for further use.^[1] Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations,interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java^[8] (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons. Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate.The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps.The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.

Today will see one example application of  A Personalized Mobile Search Engine

Personalized Mobile Search Engine

ABSTRACT

We propose a personalized mobile search engine,PMSE, that captures the users’ preferences in the form of concepts by mining their click through data. Due to the importance of location information in mobile search, PMSE classifies these concepts into content concepts and location concepts. In addition, users’ locations (positioned by GPS) are used to supplement the location concepts in PMSE. The user preferences are organized in an ontology-based, multi-facet user profile, which are used to adapt a personalized ranking function for rank adaptation of future search results. To characterize the diversity of the concepts associated with a query and their relevances to the users need, four entropies are introduced to balance the weights between the content and location facets. Based on the client-server model, we also present a detailed architecture and design for implementation of PMSE. In our design, the client collects and stores locally the clickthrough data to protect privacy, whereas heavy tasks such as concept extraction, training and reranking are performed at the PMSE server. Moreover, we address the privacy issue by restricting the information in the user profile exposed to the PMSE server with two privacy parameters. We prototype PMSE on the Google Android platform. Experimental resultsshow that PMSE significantly improves the precision comparing to the baseline.

Fig . The general process flow of PMSE

INTRODUCTION

Social Network is a social structure made of individuals called nodes, which are connected by one or more specific types of interdependency, such as friendship, kinship, financial exchang dislike, sexual relationships, or relationships o beliefs, knowledge or prestige [1]. Social Network’s link represents not only the flow between personal information, but the relation status through quantitative expression. The overall graph model of Social Network is composed of many nodes and the links that connect them, and each node’s direct/indirect connection forms the entire network.However, the current Personalized Systems based on Social Network were designed and constructed under the PC and it didn’t provide the step by step transferring methods from PC to Smartphone. To solve these problems, this research actively analyzes an individual’s characteristi based on the Social Network environment and develops a Personalized Information Retrieval System which can search for what a user wants accurately. Personalized Information Retrieval System for efficient personalized information provision proposed in this study differs from existing ones in methodology as follow: Firstly, as the system is built on the basis of NFC (Near field communication), it attempts to provide its own custom service fast and easily using its information stored in NFC. Once SNS and NFC Smartphone are associated with each other, payment is made by touching a NFC tag when visiting well known restaurants, and the information recorded in SNS is supposed to provide search results customized to individual’s tastes and preferences when carrying out asearch in individualized search system. That is, typing the same search keyword may bring different search results on NFC Smartphone as individuals have different preferences. Secondly, the existing Personalized Information Retrieval System fails to analyze the search system using Smartphone in Social Network environment. With anincreasing number of web users using Smartphone and its individualized service under research, Smartphone environment does not provide user’s search rankings suited to personal preferences. For example, when a user who wants to come by a pasta restaurant offering pasta for about 10$ and listens to rock music asks fo information search via Smartphone, search results should also be prioritized and provided in favor of user’s personalization taste. But, the existing systems do not show search rankings in consideration of individual’s tastes and tastes.

REFERENCE ARTICLES 

1.  Facilitating Document Annotation using Content and Querying Value
2. F5GA Steganographic Algorithm High Capacity Despite Better Steganalysis
3. Topic Mining over Asynchronous Text Sequences
4. Building Domain Ontologies from Text for Educational Purposes
5. Online Interactive E-Learning Using Video Annotation