Data Mining Training Institute in Jalandhar

Category: Education

Presentation Description

Data mining comprise five elements which are used in the whole process of data mining technique. It extracts the transaction data and then loads that extracted and analyzed data into the data warehouse. The data is then managed in the multidimensional database. All the data used in the process of data mining is then analyzed application software. There are many different levels of analysis exists in the data mining process. These different levels involve artificial neural networks, rule induction, data visualization, decision trees, rule induction and nearest neighbor method. Data mining applications also require right kind of technological infrastructure. Data Mining 6 months training in Phagwara Jalandhar Chandigarh will train the students very well in the working of data mining. For learning the usage of data mining techniques, students must need the guidance of any expert of data mining. But data mining technology is not a thing that everyone knows, so finding a good guide of data mining in cities like Jalandhar, Phagwara and Chandigarh is not easy. E2Matrix is a highly reputed and well recognized institute that provides excellent training in data mining. One can take the Data Mining 6 months training in Phagwara Jalandhar Chandigarh from E2Matrix.


Presentation Transcript


E2MATRIX Data Mining Training in Jalandhar 6 Weeks/Months Training in Jalandhar E2Matrix Opp. Bus Stand, Parmar Complex Alongside Axis Bank, Phagwara – Punjab (India). Web: Mail: Contact : +91 9041262727


Introduction Motivation: Why data mining ? Data Mining: On what kind of data? Data mining functionality Are all the patterns interesting? Classification of data mining systems Major issues in data mining

Motivation: “Necessity is the Mother of Invention”:

Motivation: “Necessity is the Mother of Invention” Data explosion problem Automated data collection tools and mature database technology lead to tremendous amounts of data stored in databases, data warehouses and other information repositories We are drowning in data, but starving for knowledge! Solution: Data warehousing and data mining Data warehousing and on-line analytical processing Extraction of interesting knowledge (rules, regularities, patterns, constraints) from data in large databases

Evolution of Database Technology :

Evolution of Database Technology 1960s: Data collection, database creation, IMS and network DBMS 1970s: Relational data model, relational DBMS implementation 1980s: RDBMS, advanced data models (extended-relational, OO, deductive, etc.) and application-oriented DBMS (spatial, scientific, engineering, etc.) 1990s —2000s : Data mining and data warehousing , multimedia databases, and Web databases

What Is Data Mining?:

What Is Data Mining ? Data mining (knowledge discovery in databases): Extraction of interesting ( non-trivial, implicit , previously unknown and potentially useful) information or patterns from data in large databases Alternative names Knowledge discovery(mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc. What is not data mining? (Deductive) query processing. Expert systems or small ML/statistical programs

Why Data Mining? — Potential Applications:

Why Data Mining ? — Potential Applications Database analysis and decision support Market analysis and management target marketing, customer relation management, market basket analysis, cross selling, market segmentation Risk analysis and management Forecasting, customer retention, improved underwriting, quality control, competitive analysis Fraud detection and management Other Applications Text mining (news group, email, documents) and Web analysis. Intelligent query answering

Market Analysis and Management (1):

Market Analysis and Management (1) Where are the data sources for analysis? Credit card transactions, loyalty cards, discount coupons, customer complaint calls, plus (public) lifestyle studies Target marketing Find clusters of “model” customers who share the same characteristics: interest, income level, spending habits, etc. Determine customer purchasing patterns over time Conversion of single to a joint bank account: marriage, etc. Cross-market analysis Associations/co-relations between product sales Prediction based on the association information

Market Analysis and Management (2):

Market Analysis and Management (2) Customer profiling data mining can tell you what types of customers buy what products (clustering or classification) Identifying customer requirements identifying the best products for different customers use prediction to find what factors will attract new customers Provides summary information various multidimensional summary reports statistical summary information (data central tendency and variation)

Corporate Analysis and Risk Management:

Corporate Analysis and Risk Management Finance planning and asset evaluation cash flow analysis and prediction contingent claim analysis to evaluate assets cross-sectional and time series analysis (financial-ratio, trend analysis, etc.) Resource planning: summarize and compare the resources and spending Competition: monitor competitors and market directions group customers into classes and a class-based pricing procedure set pricing strategy in a highly competitive market

Fraud Detection and Management (1):

Fraud Detection and Management (1) Applications widely used in health care, retail, credit card services, telecommunications (phone card fraud), etc. Approach use historical data to build models of fraudulent behavior and use data mining to help identify similar instances Examples auto insurance : detect a group of people who stage accidents to collect on insurance money laundering : detect suspicious money transactions (US Treasury's Financial Crimes Enforcement Network) medical insurance : detect professional patients and ring of doctors and ring of references

Fraud Detection and Management (2):

Fraud Detection and Management (2 ) Detecting inappropriate medical treatment Australian Health Insurance Commission identifies that in many cases blanket screening tests were requested (save Australian $1m/yr). Detecting telephone fraud Telephone call model: destination of the call, duration, time of day or week. Analyze patterns that deviate from an expected norm. British Telecom identified discrete groups of callers with frequent intra-group calls, especially mobile phones, and broke a multimillion dollar fraud. Retail Analysts estimate that 38% of retail shrink is due to dishonest employees.

Other Applications:

Other Applications Sports IBM Advanced Scout analyzed NBA game statistics (shots blocked, assists, and fouls) to gain competitive advantage for New York Knicks and Miami Heat Astronomy JPL and the Palomar Observatory discovered 22 quasars with the help of data mining Internet Web Surf-Aid IBM Surf-Aid applies data mining algorithms to Web access logs for market-related pages to discover customer preference and behavior pages, analyzing effectiveness of Web marketing, improving Web site organization, etc.

Data Mining: A KDD Process:

Data Mining: A KDD Process Data mining: the core of knowledge discovery process. Data Cleaning Data Integration Databases Data Warehouse Knowledge Task-relevant Data Selection Data Mining Pattern Evaluation

Steps of a KDD Process :

Steps of a KDD Process Learning the application domain: relevant prior knowledge and goals of application Creating a target data set: data selection Data cleaning and preprocessing: (may take 60% of effort!) Data reduction and transformation : Find useful features, dimensionality/variable reduction, invariant representation. Choosing functions of data mining summarization, classification, regression, association, clustering. Choosing the mining algorithm(s) Data mining : search for patterns of interest Pattern evaluation and knowledge presentation visualization, transformation, removing redundant patterns, etc. Use of discovered knowledge

Data Mining and Business Intelligence :

Data Mining and Business Intelligence Increasing potential to support business decisions End User Business Analyst Data Analyst DBA Making Decisions Data Presentation Visualization Techniques Data Mining Information Discovery Data Exploration OLAP, MDA Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts Data Sources Paper, Files, Information Providers, Database Systems, OLTP

Architecture of a Typical Data Mining System

Architecture of a Typical Data Mining System Data Warehouse Data cleaning & data integration Filtering Databases Database or data warehouse server Data mining engine Pattern evaluation Graphical user interface Knowledge-base

Data Mining: On What Kind of Data?:

Data Mining: On What Kind of Data? Relational databases Data warehouses Transactional databases Advanced DB and information repositories Object-oriented and object-relational databases Spatial databases Time-series data and temporal data Text databases and multimedia databases Heterogeneous and legacy databases WWW

Data Mining Functionalities (1):

Data Mining Functionalities (1) Concept description: Characterization and discrimination Generalize, summarize, and contrast data characteristics, e.g., dry vs. wet regions Association ( correlation and causality) Multi-dimensional vs. single-dimensional association age(X, “20..29”) ^ income(X, “20..29K”) à buys(X, “PC”) [support = 2%, confidence = 60%] contains(T, “computer”) à contains(x, “software”) [1%, 75%]

Data Mining Functionalities (2):

Data Mining Functionalities (2) Classification and Prediction Finding models (functions) that describe and distinguish classes or concepts for future prediction E.g., classify countries based on climate, or classify cars based on gas mileage Presentation: decision-tree, classification rule, neural network Prediction: Predict some unknown or missing numerical values Cluster analysis Class label is unknown: Group data to form new classes, e.g., cluster houses to find distribution patterns Clustering based on the principle: maximizing the intra-class similarity and minimizing the interclass similarity

Data Mining Functionalities (3):

Data Mining Functionalities (3) Outlier analysis Outlier: a data object that does not comply with the general behavior of the data It can be considered as noise or exception but is quite useful in fraud detection, rare events analysis Trend and evolution analysis Trend and deviation: regression analysis Sequential pattern mining, periodicity analysis Similarity-based analysis Other pattern-directed or statistical analyses

Are All the “Discovered” Patterns Interesting?:

Are All the “Discovered” Patterns Interesting? A data mining system/query may generate thousands of patterns, not all of them are interesting. Suggested approach: Human-centered, query-based, focused mining Interestingness measures : A pattern is interesting if it is easily understood by humans, valid on new or test data with some degree of certainty, potentially useful , novel, or validates some hypothesis that a user seeks to confirm Objective vs. subjective interestingness measures: Objective: based on statistics and structures of patterns, e.g., support, confidence, etc. Subjective: based on user’s belief in the data, e.g., unexpectedness, novelty, actionability, etc.

Can We Find All and Only Interesting Patterns?:

Can We Find All and Only Interesting Patterns? Find all the interesting patterns: Completeness Can a data mining system find all the interesting patterns? Association vs. classification vs. clustering Search for only interesting patterns: Optimization Can a data mining system find only the interesting patterns? Approaches First general all the patterns and then filter out the uninteresting ones. Generate only the interesting patterns — mining query optimization

Data Mining: Confluence of Multiple Disciplines :

Data Mining: Confluence of Multiple Disciplines Data Mining Database Technology Statistics Other Disciplines Information Science Machine Learning Visualization

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