Java Abs Building Intelligent Shopping Assistant Using Data Mining


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Java Abs Building Intelligent Shopping Assistant Using Data Mining

  1. 1. BUILDING INTELLIGENT SHOPPING ASSISTANT USING DATA MINING INTRODUCTION ABOUT DATA MINING Generally, data mining otherwise called as data or knowledge discovery is the process of analyzing data from different perspectives and summarizing it into useful information namely the information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. Although data mining is a relatively new term, the technology is not. Companies have used powerful computers to sift through volumes of supermarket scanner data and analyze market research reports for years. However, continuous innovations in computer processing power, disk storage, and statistical software are dramatically increasing the accuracy of analysis while driving down the cost. DATA, INFORMATION, AND KNOWLEDGE DATA Data are any facts, numbers, or text that can be processed by a computer. Today, organizations are accumulating vast and growing amounts of data in different formats and different databases. This includes: • Operational or transactional data such as, sales, cost, inventory, payroll, and accounting • Non-operational data, such as industry sales, forecast data, and macro economic data • Meta data - data about the data itself, such as logical database design or data dictionary definitions INFORMATION The patterns, associations, or relationships among all this data can provide information. For example, analysis of retail point of sale transaction data can yield information on which products are selling and when
  2. 2. KNOWLEDGE Information can be converted into knowledge about historical patterns and future trends. For example, summary information on retail supermarket sales can be analyzed in light of promotional efforts to provide knowledge of consumer buying behavior. Thus, a manufacturer or retailer could determine which items are most susceptible to promotional efforts. DATA WAREHOUSES Dramatic advances in data capture, processing power, data transmission, and storage capabilities are enabling organizations to integrate their various databases into data warehouses. Data warehousing is defined as a process of centralized data management and retrieval. Data warehousing, like data mining, is a relatively new term although the concept itself has been around for years. Data warehousing represents an ideal vision of maintaining a central repository of all organizational data. Centralization of data is needed to maximize user access and analysis. Dramatic technological advances are making this vision a reality for many companies. And, equally dramatic advances in data analysis software are allowing users to access this data freely. The data analysis software is what supports data mining. WHAT CAN DATA MINING DO? Companies with a strong consumer focus - retail, financial, communication, and marketing organizations, primarily use data mining today. It enables these companies to determine relationships among "internal" factors such as price, product positioning, or staff skills, and "external" factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to "drill down" into summary information to view detail transactional data. With data mining, a retailer could use point-of-sale records of customer purchases to send targeted promotions based on an individual's purchase history. By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to appeal to specific customer segments. HOW DOES DATA MINING WORK? While large-scale information technology has been evolving separate transaction and analytical systems, data mining provides the link between the two. Data mining software analyzes relationships and patterns in stored transaction data based on open-ended user queries. Several types of analytical software are available: statistical, machine learning, and neural networks.
  3. 3. Generally, any of four types of relationships are sought: • Classes: Stored data is used to locate data in predetermined groups. For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically order. This information could be used to increase traffic by having daily specials. • Clusters: Data items are grouped according to logical relationships or consumer preferences. For example, data can be mined to identify market segments or consumer affinities. • Associations: Data can be mined to identify associations. • Sequential patterns: Data is mined to anticipate behavior patterns and trends. For example, an outdoor equipment retailer could predict the likelihood of a backpack being purchased based on a consumer's purchase of sleeping bags and hiking shoes. DATA MINING CONSISTS OF FIVE MAJOR ELEMENTS • Extract, transform, and load transaction data onto the data warehouse system. • Store and manage the data in a multidimensional database system. • Provide data access to business analysts and information technology professionals. • Analyze the data by application software. • Present the data in a useful format, such as a graph or table. AIM/OBJECTIVE OF THE SYSTEM The aim or objective of the proposed system, is to build a prototype for intelligent shopping assistant using data mining that aids supermarket shoppers and presents personalized promotions during the course of their visit. Hence the software will act as an easier promotion tool that will contain transaction based purchase data for predicting shopping lists for a large number of grocery customers. The proposed software’s primary objective is to predict the sampled data with market basket analysis. This enables a supermarket or shopkeeper to analyze the type of purchase made by the customer. EXISTING SYSTEM Generally a consumer-shopping store is an ideal environment to explore ubiquitous computing applications. Each week, millions of shoppers enters the store, in which they are immersed with tens of thousands of distinct products choices, from which ultimately they only select a few dozen of items
  4. 4. In all the supermarkets or consumer shopping models, the shop might not be able to predict the kind of purchase made by the frequent customers In this kind of model, the frequent customers might have a prominent list of items, wherein the customers can be given flexibility by random choice of the items displayed So whenever the customers come, they do not have any facility for easy shopping Moreover the shopkeepers or the sales personnel might not be able to make any predictions or association Such a numerous database cannot be handled in a normal database queried. Hence we are in an urge to develop a software prototype model, which will be an intelligent, shopping assistant using data mining that aids in consumer store shoppers and persistent personalized promotions during the course of their visit using predictions. PROPOSED SYSTEM The proposed system is a software using data mining which will be having a core capability by using learning algorithms to build consumer models for each individual customer. The main aim is to ground promotions in the context of an application that is viewed as a useful tool by the customer white providing business benefits to set retailers and packaged good companies. THE ALGORITHMS USED ARE AS FOLLOWS 1) Similarity Clustering 2) Classifier Meta Classifier Algorithm 3) Data Segmentation Algorithm 4) Precision and Prediction Algorithms using Decision Trees Once a model has been created by a data mining application, the model can then be used to make predictions for new data. The process of using the model is distinct from the process that creates the model. Typically, a model is used multiple times after it is created to score different databases. For example, consider a model that has been created to predict the probability that a customer will purchase something from a catalog if it is sent to them. The model would be built by using historical data from customers and prospects that were sent catalogs, as well as information about what they bought (if anything) from the catalogs. During the model-building process, the data mining application would use information about the existing customers to build and validate the model.
  5. 5. In the end, the result is a model that would take details about the customer (or prospects) as inputs and generate a number between 0 and 1 as the output The proposed system offers an inexpensive way to advertise and market products and services and functions as a low cost, high impact channel for your business. These standards, which allow computers in different organizations to share information over privately built, closed networks known as value-added networks, helps for the easy use of online corporate purchasing. PROPOSED SYSTEM HARDWARE REQUIREMENTS HARDWARE Processor - PIII or higher processor RAM - 128 MB or higher HDD - 40 GB or higher FDD - 1.44 MB MONITOR - LG/SAMSUNG colour Keyboard / Mouse / ATX Cabinet SOFTWARE OPERATING SYSTEM : WIN 2000/WIN XP/WIN 98 SOFTWARE : JDK 1.3 OR HIGHER DATABASE : Oracle 8i MODULES TO BE IMPLEMENTED 1) BUILDING KNOWLEDGE BASE 2) TRANSACTION MONITOR 3) SIMILARITY CLUSTERING 4) CLASSIFICATION USING META CLASSIFICATION 5) DATA SEGMENTATION 6) DECISION TREES 7) PREDICTION ANALYSIS