Knowledge Discovery
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Knowledge discovery involves non-trivially extracting useful and previously unknown information from data. It includes data mining to detect patterns in prepared data. Knowledge discovery can be divided into classification, numerical prediction, association, and clustering and is used for applications like financial forecasting, targeted marketing, medical diagnosis, and fraud detection. Data mining can use labeled data with known attributes to predict unknown attributes through supervised learning, or unlabeled data without known attributes through unsupervised learning such as association rules and clustering.
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Data mining
my topic is data mining technique.. what is data mining,and its type,application are included in this presentation..
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Data Mining: Text and web mining
This document discusses text and web mining. It defines text mining as analyzing huge amounts of text data to extract information. It discusses measures for text retrieval like precision and recall. It also covers text retrieval and indexing methods like inverted indices and signature files. Finally, it discusses challenges in web mining like the huge size and dynamic nature of the web and how web usage mining allows collection of web access information from server logs.
Data Mining: Outlier analysis
Outlier analysis is used to identify outliers, which are data objects that are inconsistent with the general behavior or model of the data. There are two main types of outlier detection - statistical distribution-based detection, which identifies outliers based on how far they are from the average statistical distribution, and distance-based detection, which finds outliers based on how far they are from other data objects. Outlier analysis is useful for tasks like fraud detection, where outliers may indicate fraudulent activity that is different from normal patterns in the data.
Data Mining: Mining stream time series and sequence data
This document discusses various methodologies for processing and analyzing stream data, time series data, and sequence data. It covers topics such as random sampling and sketches/synopses for stream data, data stream management systems and queries, the Hoeffding tree and Very Fast Decision Tree (VFDT) algorithms for classification, ensemble methods and concept drift, clustering of evolving data streams, trend analysis and similarity search for time series data, Markov chains for sequence analysis, and algorithms like the forward algorithm, Viterbi algorithm, and Baum-Welch algorithm for hidden Markov models.
Data Mining: Mining ,associations, and correlations
Market basket analysis examines customer purchasing patterns to determine which items are commonly bought together. This can help retailers with marketing strategies like product bundling and complementary product placement. Association rule mining is a two-step process that first finds frequent item sets that occur together above a minimum support threshold, and then generates strong association rules from these frequent item sets based on minimum support and confidence. Various techniques can improve the efficiency of the Apriori algorithm for mining association rules, such as hashing, transaction reduction, partitioning, sampling, and dynamic item-set counting. Pruning strategies like item merging, sub-item-set pruning, and item skipping can also enhance efficiency. Constraint-based mining allows users to specify constraints on the type of
Data Mining: Graph mining and social network analysis
Graph mining analyzes structured data like social networks and the web through graph search algorithms. It aims to find frequent subgraphs using Apriori-based or pattern growth approaches. Social networks exhibit characteristics like densification and heavy-tailed degree distributions. Link mining analyzes heterogeneous, multi-relational social network data through tasks like link prediction and group detection, facing challenges of logical vs statistical dependencies and collective classification. Multi-relational data mining searches for patterns across multiple database tables, including multi-relational clustering that utilizes information across relations.
Data Mining: Data warehouse and olap technology
This document discusses data warehousing and online analytical processing (OLAP) technology. It defines a data warehouse, compares it to operational databases, and explains how OLAP systems organize and present data for analysis. The document also describes multidimensional data models, common OLAP operations, and the steps to design and construct a data warehouse. Finally, it discusses applications of data warehouses and efficient processing of OLAP queries.
Data MIning: Data processing
Data processing involves cleaning, integrating, transforming, reducing, and summarizing data from various sources into a coherent and useful format. It aims to handle issues like missing values, noise, inconsistencies, and volume to produce an accurate and compact representation of the original data without losing information. Some key techniques involved are data cleaning through binning, regression, and clustering to smooth or detect outliers; data integration to combine multiple sources; data transformation through smoothing, aggregation, generalization and normalization; and data reduction using cube aggregation, attribute selection, dimensionality reduction, and discretization.
Data Mining: clustering and analysis
Clustering is the process of grouping similar objects together. It allows data to be analyzed and summarized. There are several methods of clustering including partitioning, hierarchical, density-based, grid-based, and model-based. Hierarchical clustering methods are either agglomerative (bottom-up) or divisive (top-down). Density-based methods like DBSCAN and OPTICS identify clusters based on density. Grid-based methods impose grids on data to find dense regions. Model-based clustering uses models like expectation-maximization. High-dimensional data can be clustered using subspace or dimension-reduction methods like CLIQUE and PROCLUS. Constraint-based clustering allows users to specify preferences or constraints.
Data mining: Classification and Prediction
This document discusses various machine learning techniques for classification and prediction. It covers decision tree induction, tree pruning, Bayesian classification, Bayesian belief networks, backpropagation, association rule analysis, and ensemble methods like bagging and boosting. It also discusses key considerations for classification and prediction like accuracy, speed, robustness, and scalability. The goal is to construct models or classifiers that can predict categorical labels for classification tasks or continuous values for prediction tasks.
Data Mining: Data mining classification and analysis
Data mining involves classification, cluster analysis, outlier mining, and evolution analysis. Classification models data to distinguish classes using techniques like decision trees or neural networks. Cluster analysis groups similar objects without labels, while outlier mining finds irregular objects. Evolution analysis models changes over time. Data mining performance depends on algorithm efficiency and scalability for large datasets across diverse database types.
Data Mining: Data mining and key definitions
Data mining is the process of analyzing large amounts of data to discover hidden patterns and relationships. It involves collecting data, removing noise, focusing on relevant portions, analyzing the data, and extracting useful information. Key components of a data mining system include a data warehouse server, information repository, user interface, data mining engine, and pattern evaluation module. A data warehouse is a centralized repository where data is stored and organized for analysis from multiple sources.
Data Mining: Data cube computation and data generalization
Data generalization abstracts data from a low conceptual level to higher levels. Different cube materialization methods include full, iceberg, closed, and shell cubes. The Apriori property states that if a cell does not meet minimum support, neither will its descendants, and can reduce iceberg cube computation. BUC constructs cubes from the apex downward, allowing pruning using Apriori and sharing partitioning costs. Discovery-driven exploration assists users in intelligently exploring aggregated data cubes. Constrained gradient analysis incorporates significance, probe, and gradient constraints to reduce the search space. Attribute-oriented induction generalizes based on attribute values to characterize data. Attribute generalization is controlled through thresholds and relations.
Data Mining: Applying data mining
This document discusses different types of data mining including object mining, spatial mining, text mining, web mining, and multimedia mining. It describes how data mining can be used to analyze object-relational and object-oriented databases by generalizing set-valued attributes, aggregating and approximating spatial and multimedia data, generalizing object identifiers and class hierarchies. The document also discusses spatial databases, spatial data mining, spatial data warehouses, mining spatial associations and co-locations, mining raster databases, multimedia databases, and approaches for multimedia data mining and analysis.
Data Mining: Application and trends in data mining
This document provides an overview of application trends in data mining. It discusses how data mining is used for financial data analysis, customer analysis in retail and telecommunications, biological data analysis, scientific research, intrusion detection, and more. It also outlines statistical and visual data mining techniques, security considerations, and how to assess a data mining system. The document explores both current and emerging trends in data mining across various domains.
AI: Planning and AI
A situated planning agent treats planning and acting as a single process rather than separate processes. It uses conditional planning to construct plans that account for possible contingencies by including sensing actions. The agent resolves any flaws in the conditional plan before executing the appropriate actions based on the conditions that are sensed to be true. When facing uncertainty, the agent must have preferences between outcomes to make decisions using utility theory and represent probabilities using a joint probability distribution over variables in the domain.
AI: Logic in AI 2
A simple planning agent uses percepts from the environment to build a model of the current state and calls a planning algorithm to generate a plan to achieve its goal. Practical planning involves restricting the language used to define problems, using specialized planners rather than general theorem provers, and adopting hierarchical decomposition to store and retrieve abstract plans from a library. A solution is a fully instantiated, totally ordered plan that guarantees achieving the goal.
AI: Logic in AI
The document discusses different types of logical reasoning systems used in artificial intelligence, including knowledge-based agents, first-order logic, higher-order logic, goal-based agents, knowledge engineering, and description logics. It provides examples of objects, properties, relations, and functions that can be represented and reasoned about logically. It also compares different approaches to logical indexing and storage of knowledge bases.
AI: Learning in AI 2
Bayesian learning views hypotheses as intermediaries between data and predictions. Belief networks can have known or unknown structures and hidden variables. Belief networks represent knowledge locally while neural networks use distributed representations. Reinforcement learning uses rewards to learn successful agent functions, like Q-learning which learns action-value functions. Active learning agents consider actions, outcomes, and how actions affect rewards received to build an environment model and utility function. Explanation based learning speeds up programs by reusing results of prior computations to make knowledge bases more efficient.
AI: Learning in AI
Neural networks can be used for machine learning tasks like classification. They consist of interconnected nodes that update their weight values during a training process using examples. Neural networks have been applied successfully to tasks like handwritten character recognition, autonomous vehicle control by observing human drivers, and pronunciation of written text. Their design is inspired by biological neural networks in the brain.
AI: Introduction to artificial intelligence
This document provides an introduction to artificial intelligence including definitions of AI, categories of AI systems, requirements for an artificially intelligent system, a brief history of AI, examples of AI in the real world, definitions of intelligent agents and different types of agent programs. It defines AI as the study of intelligent behavior in computational processes and discusses systems that think like humans, think rationally, act like humans, and act rationally. It also outlines the requirements for an AI system to have natural language processing, knowledge representation, automated reasoning, and machine learning.
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Knowledge Discovery
- 2. Definition Definition – “Non-trivial extraction of implicit, previously unknown and potentially useful information from data.” Data Mining – Responsible for detecting patterns from the pre-processed (prepared) data. It is only a part of Knowledge discovery process.
- 3. Applications Can be divided into four major kinds: Classification Numerical prediction Association Clustering Some examples: Automatic abstraction Financial forecasting Targeted marketing Medical diagnosis Credit card fraud detection Weather forecasting etc.
- 4. Labeled & Unlabeled data General Terminology: Instances – Dataset of examples Attributes – Variables in an instance Labeled data Specific attribute whose value in some instances can be used to predict its value in unknown instances Unlabeled data No such specific attribute that can be used to predict the value in unknown instances. Supervised learning – Data mining using labeled data Unsupervised learning – Data mining using unlabeled data
- 5. Labeled data Attributes can be of two types: Categorical attribute Takes a value from only a fixed set of values (like an enumeration) eg. ‘very good’, ‘good’, ‘poor’ Supervised learning is called Classification Numerical attribute Can take a value from a continuous range of numerical values Supervised learning is called Regression
- 6. Unlabeled data It doesn’t have any specifically designated attribute Unsupervised learning Data mining using unlabeled data Purpose - To extract as much as it is possible from the data available.
- 7. Supervised learning: Classification It is based on the following three methods: Nearest neighbor matching: Identifying the classified instances that are closest (in some sense) to the unclassified one Classification rules: Look for rules that can be used to predict the classification of an unknown instance Classification tree: Generation of classification rules via the tree-like structure
- 8. Supervised Learning: Numerical Prediction Regression is done by using Neural Networks Neural Network: Given a set of inputs to predict one or more outputs
- 9. Unsupervised Learning: Association Rules Association Rules: To find any relationship that exists amongst the values of variables within a training set Example: IF variable_1>90 and switch_6 = open THEN variable_3 < 47.5 and switch_9 = closed (probability = 0.8)
- 10. Unsupervised Learning: Clustering To find groups of items that are similar Example: A company may group its customers based on income to target its policies etc.
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