Posts: 610
Threads: 262
Joined: Jun 2010
1.Frequent Closed Sequence Mining without Candidate Maintenance
2.Maintaining Strong Cache Consistency for the Domain Name System
3.Efficient Skyline and Top-k Retrieval in Subspaces
4.Efficient Process of Top-k Range-Sum Queries over Multiple Streams with Minimized Global Error
5.Fast Nearest Neighbor Condensation for Large Data Sets Classification
6.An Exploratory Study of Database Integration Processes
7.COFI approach for Mining Frequent Item sets
8.Online Random Shuffling of Large Database Tables
9.A Flexible Content Adaptation System Using a Rule-Based Approach
10.Efficient Revalidation of XML Documents
11.Negative Samples Analysis in Relevance Feedback
12.Bayesian Networks for Knowledge-Based Authentication
13.Continuous Nearest Neighbor Queries over Sliding Windows
14.The Concentration of Fractional Distances
15.Classifier Ensembles with a Random Linear Oracle
16.An Efficient Web Page Change Detection System Based on an Optimized Hungarian Algorithm
17.Mining Nonambiguous Temporal Patterns for Interval-Based Events
18.Peer-to-Peer in Metric Space and Semantic Space
19.Adaptive Index Utilization in Memory-Resident Structural Joins
20.On Three Types of Covering-Based Rough Sets
21.Discovering Frequent Generalized Episodes When Events Persist for Different Durations
22.Efficient and Scalable Algorithms for Inferring Likely Invariants in Distributed Systems
23.Mining Closed Frequent item set using CHARM algorithm
24.Integrating Constraints and Metric Learning in Semi-Supervised Clustering
25.Foundational Approach to Mining Item set Utilities from Databases
26.Fast Frequent Pattern Mining
27.Efficient Computation of Iceberg Cubes by Bounding Aggregate Functions
28.A Note on Linear Time Algorithms for Maximum Error Histograms
29.Toward Exploratory Test-Instance-Centered Diagnosis in High-Dimensional Classification
30.An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data
31.A Method for Estimating the Precision of Place name Matching
32.Practical Algorithms and Lower Bounds for Similarity Search in Massive Graphs
33.Mining Confident Rules without Support Requirement
34.Mining Frequent Item set without Support Threshold
35.unified framework for utility based measures
36.An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval
37.The Google Similarity Distance
38.Reverse Nearest Neighbors Search in Ad Hoc Subspaces
39.Quality-Aware Sampling and Its Applications in Incremental Data Mining
40.An Exact Data Mining Method for Finding Center Strings and All Their Instances
41.Enhancing the Effectiveness of Clustering with Spectra Analysis
42.Efficient Monitoring Algorithm for Fast News Alerts
43.Top-k Monitoring in Wireless Sensor Networks
44.Wildcard Search in Structured Peer-to-Peer Networks
45.Neural-Based Learning Classifier Systems
46.Discovering Frequent Agreement Sub trees from Phylogenetic Data
47.Watermarking Relational Databases Using Optimization-Based Techniques
48.Efficient Approximate Query Processing in Peer-to-Peer Networks
49.Ontology-Based Service Representation and Selection
50.Compressed Hierarchical Mining of Frequent Closed Patterns from Dense Data Sets
51.Semi-supervised Regression with Co-training-Style Algorithms
52.Evaluation of Clustering with Banking Credit Card segment
53.An efficient clustering algorithm for huge dimensional database
54.Novel approach for Targeted Association Querying
55.Hiding Sensitive Association Rules with Limited Side Effects
56.A Relation-Based Search Engine in Semantic Web
57.Extracting Actionable Knowledge from Decision Trees
58.A Requirements Driven Framework for Benchmarking Semantic Web Knowledge Base Systems
59.The Threshold Algorithm: From Middleware Systems to the Relational Engine
60.Rank Aggregation for Automatic Schema Matching
61.Rule Extraction from Support Vector Machines: A Sequential Covering Approach
62.Efficiently Querying Large XML Data Repositories: A Survey
63.Graph-Based Analysis of Human Transfer Learning Using a Game Tested
64.Evaluating Universal Quantification in XML
65.Customer Profiling & Segmentation using Data Mining Techniques
66.Efficient Frequent Item set Mining Using Global Profit Weighted (GPW) Support Threshold
67.Fast Algorithms for Frequent Item set Mining using FP-Trees
68.Evaluation for Mining Share Frequent item sets Containing Infrequent Subsets
Posts: 2,300
Threads: 878
Joined: Sep 2010
[attachment=4763]
data mining project ideas
By
Mr. TARUN KUMAR NAGAL
Under Guidance Of
Prof. RAHUL KHOKALE
( HOD CMPN Dept.)
2009-2010
Thakur College of Engineering and Technology
Kandivali (East)
Mumbai-101
abstract
Data mining is the process of extracting patterns from data. Data mining is becoming an increasingly important tool to transform this data into information. It is commonly used in a wide range of profiling practices, such as marketing, surveillance, fraud detection and scientific discovery.
Data mining can be used to uncover patterns in data but is often carried out only on samples of data. The mining process will be ineffective if the samples are not a good representation of the larger body of data. Data mining cannot discover patterns that may be present in the larger body of data if those patterns are not present in the sample being "mined". Inability to find patterns may become a cause for some disputes between customers and service providers. Therefore data mining is not fool proof but may be useful if sufficiently representative data samples are collected. The discovery of a particular pattern in a particular set of data does not necessarily mean that a pattern is found elsewhere in the larger data from which that sample was drawn. An important part of the process is the verification and validation of patterns on other samples of data.
The related terms data dredging, data fishing and data snooping refer to the use of data mining techniques to sample sizes that are (or may be) too small for statistical inferences to be made about the validity of any patterns discovered (see also data-snooping bias). Data dredging may, however, be used to develop new hypotheses, which must then be validated with sufficiently large sample sets.
http://studentbank.in/report-data-mining...a-proposal
Posts: 2,300
Threads: 878
Joined: Sep 2010
[attachment=5639]
data mining project ideas
data mining project ideas
1.Frequent Closed Sequence Mining without Candidate Maintenance
2.Maintaining Strong Cache Consistency for the Domain Name System
3.Efficient Skyline and Top-k Retrieval in Subspaces
4.Efficient Process of Top-k Range-Sum Queries over Multiple Streams with Minimized Global Error
5.Fast Nearest Neighbor Condensation for Large Data Sets Classification
6.An Exploratory Study of Database Integration Processes
7.COFI approach for Mining Frequent Item sets
8.Online Random Shuffling of Large Database Tables
9.A Flexible Content Adaptation System Using a Rule-Based Approach
10.Efficient Revalidation of XML Documents
11.Negative Samples Analysis in Relevance Feedback
12.Bayesian Networks for Knowledge-Based Authentication
13.Continuous Nearest Neighbor Queries over Sliding Windows
14.The Concentration of Fractional Distances
15.Classifier Ensembles with a Random Linear Oracle
16.An Efficient Web Page Change Detection System Based on an Optimized Hungarian Algorithm
17.Mining Nonambiguous Temporal Patterns for Interval-Based Events
18.Peer-to-Peer in Metric Space and Semantic Space
19.Adaptive Index Utilization in Memory-Resident Structural Joins
20.On Three Types of Covering-Based Rough Sets
21.Discovering Frequent Generalized Episodes When Events Persist for Different Durations
22.Efficient and Scalable Algorithms for Inferring Likely Invariants in Distributed Systems
23.Mining Closed Frequent item set using CHARM algorithm
24.Integrating Constraints and Metric Learning in Semi-Supervised Clustering
25.Foundational Approach to Mining Item set Utilities from Databases
26.Fast Frequent Pattern Mining
27.Efficient Computation of Iceberg Cubes by Bounding Aggregate Functions
28.A Note on Linear Time Algorithms for Maximum Error Histograms
29.Toward Exploratory Test-Instance-Centered Diagnosis in High-Dimensional Classification
30.An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data
31.A Method for Estimating the Precision of Place name Matching
32.Practical Algorithms and Lower Bounds for Similarity Search in Massive Graphs
33.Mining Confident Rules without Support Requirement
34.Mining Frequent Item set without Support Threshold
35.unified framework for utility based measures
36.An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval
37.The Google Similarity Distance
38.Reverse Nearest Neighbors Search in Ad Hoc Subspaces
39.Quality-Aware Sampling and Its Applications in Incremental Data Mining
40.An Exact Data Mining Method for Finding Center Strings and All Their Instances
41.Enhancing the Effectiveness of Clustering with Spectra Analysis
42.Efficient Monitoring Algorithm for Fast News Alerts
43.Top-k Monitoring in Wireless Sensor Networks
44.Wildcard Search in Structured Peer-to-Peer Networks
45.Neural-Based Learning Classifier Systems
46.Discovering Frequent Agreement Sub trees from Phylogenetic Data
47.Watermarking Relational Databases Using Optimization-Based Techniques
48.Efficient Approximate Query Processing in Peer-to-Peer Networks
49.Ontology-Based Service Representation and Selection
50.Compressed Hierarchical Mining of Frequent Closed Patterns from Dense Data Sets
51.Semi-supervised Regression with Co-training-Style Algorithms
52.Evaluation of Clustering with Banking Credit Card segment
53.An efficient clustering algorithm for huge dimensional database
54.Novel approach for Targeted Association Querying
55.Hiding Sensitive Association Rules with Limited Side Effects
56.A Relation-Based Search Engine in Semantic Web
57.Extracting Actionable Knowledge from Decision Trees
58.A Requirements Driven Framework for Benchmarking Semantic Web Knowledge Base Systems
59.The Threshold Algorithm: From Middleware Systems to the Relational Engine
60.Rank Aggregation for Automatic Schema Matching
61.Rule Extraction from Support Vector Machines: A Sequential Covering Approach
62.Efficiently Querying Large XML Data Repositories: A Survey
63.Graph-Based Analysis of Human Transfer Learning Using a Game Tested
64.Evaluating Universal Quantification in XML
65.Customer Profiling & Segmentation using Data Mining Techniques
66.Efficient Frequent Item set Mining Using Global Profit Weighted (GPW) Support Threshold
67.Fast Algorithms for Frequent Item set Mining using FP-Trees
68.Evaluation for Mining Share Frequent item sets Containing Infrequent Subsets
Posts: 3,561
Threads: 923
Joined: Sep 2010
Prepared by:
GOWDHAMI.D
M.VEERAMANIMEKALA
[attachment=6698]
DATA MINING-DEFINITION
Data mining is the process of processing large volumes of data (usually stored in a database), searching for patterns and relationships within that data. Retail outlets using data mining might discover that many customers who buy beer also buy diapers. They may then increase sales by positioning the two together.
WHY DATA MINING
Data Mining helps extract information such as
Credit ratings/targeted marketing
Fraud detection
Customer relationship management
Posts: 1
Threads: 0
Joined: Mar 2011
i would like to know the latest data mining research areas
Posts: 5,362
Threads: 2,998
Joined: Feb 2011
[attachment=11768]
Why Mine Data? Commercial Viewpoint
l Lots of data is being collected
and warehoused
– Web data, e-commerce
– purchases at department/
grocery stores
– Bank/Credit Card
transactions
l Computers have become cheaper and more powerful
l Competitive Pressure is Strong
– Provide better, customized services for an edge (e.g. in Customer Relationship Management)
Why Mine Data? Scientific Viewpoint
l Data collected and stored at
enormous speeds (GB/hour)
– remote sensors on a satellite
– telescopes scanning the skies
– microarrays generating gene
expression data
– scientific simulations
generating terabytes of data
l Traditional techniques infeasible for raw data
l Data mining may help scientists
– in classifying and segmenting data
– in Hypothesis Formation
Mining Large Data Sets – Motivation
l There is often information “hidden” in the data that is
not readily evident
l Human analysts may take weeks to discover useful information
l Much of the data is never analyzed at all
What is Data Mining?
l Many Definitions
– Non-trivial extraction of implicit, previously unknown and potentially useful information from data
– Exploration & analysis, by automatic or
semi-automatic means, of
large quantities of data
in order to discover
meaningful patterns
What is (not) Data Mining?
l What is not Data Mining?
– Look up phone number in phone directory
– Query a Web search engine for information about “Amazon”
l What is Data Mining?
– Certain names are more prevalent in certain US locations (O’Brien, O’Rurke, O’Reilly… in Boston area)
– Group together similar documents returned by search engine according to their context (e.g. Amazon rainforest, Amazon.com,)
Posts: 4,190
Threads: 817
Joined: Feb 2012
