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IEEE 2017 WEB MINING/DATA MINING BASED PROJECTS
Datamining is very broad area releated to database. Here is a list of projects related to data mining which are developed using latest techniques and algorithms. Latest Data Mining topics, Latest Data Mining Concepts for Diploma, Latest Data Mining Concepts for Engineering, Latest Data Mining Concepts for M-Tech, Data Mining project centers in Bangalore with high quality Training and development, Latest J2EE Projects with recent Technology.Here is a list of project ideas for Data Mining concepts. Students belonging to third year mini projects or final year projects can use these projects as mini-projects as well as mega-projects. If you have questions regarding these projects feel free to contct us. You may also ask for abstract of a project idea that you have or want to work on.The own projects idea for diploma and Engineering students can also be done here.
IEEE 2017-2018 web mining/data mining project list for MTech /BE / BTech / MCA / M.sc students in bangalore.
|TED001||FIDOOP: PARALLEL MINING OF FREQUENT ITEMSETS USING MAPREDUCE||SYNOPSIS|
|TED002||SPORE: A SEQUENTIAL PERSONALIZED SPATIAL ITEM RECOMMENDER SYSTEM||SYNOPSIS|
|TED003||INVERTED LINEAR QUADTREE: EFFICIENT TOP K SPATIAL KEYWORD SEARCH||SYNOPSIS|
|TED004||TRUTH DISCOVERY IN CROWDSOURCED DETECTION OF SPATIAL EVENTS||SYNOPSIS|
|TED005||SENTIMENT ANALYSIS OF TOP COLLEGES IN INDIA USING TWITTER DATA||SYNOPSIS|
|TED006||FRAPPE: DETECTING MALICIOUS FACEBOOK APPLICATIONS||SYNOPSIS|
|TED007||A NOVEL RECOMMENDATION MODEL REGULARIZED WITH USER TRUST AND ITEM RATINGS||SYNOPSIS|
|TED008||AUTOMATICALLY MINING FACETS FOR QUERIES FROM THEIR SEARCH RESULTS||SYNOPSIS|
|TED009||BUILDING AN INTRUSION DETECTION SYSTEM USING A FILTER-BASED FEATURE SELECTION ALGORITHM||SYNOPSIS|
|TED010||CONNECTING SOCIAL MEDIA TO E-COMMERCE: COLD-START PRODUCT RECOMMENDATION USING MICROBLOGGING INFORMATION||SYNOPSIS|
|TED011||CROSS-DOMAIN SENTIMENT CLASSIFICATION USING SENTIMENT SENSITIVE EMBEDDINGS||SYNOPSIS|
|TED012||CYBERBULLYING DETECTION BASED ON SEMANTIC-ENHANCED MARGINALIZED DENOISING AUTO-ENCODER||SYNOPSIS|
|TED013||CROWDSOURCING FOR TOP-K QUERY PROCESSING OVER UNCERTAIN DATA||SYNOPSIS|
|TED014||EFFICIENT ALGORITHMS FOR MINING TOP-K HIGH UTILITY ITEMSETS||SYNOPSIS|
|TED015||DOMAIN-SENSITIVE RECOMMENDATION WITH USER-ITEM SUBGROUP ANALYSIS||SYNOPSIS|
|TED016||MINING USER-AWARE RARE SEQUENTIAL TOPIC PATTERNS IN DOCUMENT STREAMS||SYNOPSIS|
|TED017||NEAREST KEYWORD SET SEARCH IN MULTI-DIMENSIONAL DATASETS||SYNOPSIS|
|TED018||RATING PREDICTION BASED ON SOCIAL SENTIMENT FROM TEXTUAL REVIEWS||SYNOPSIS|
|TED019||LOCATION AWARE KEYWORD QUERY SUGGESTION BASED ON DOCUMENT PROXIMITY||SYNOPSIS|
|TED020||USING HASHTAG GRAPH-BASED TOPIC MODEL TO CONNECT SEMANTICALLY-RELATED WORDS WITHOUT CO-OCCURRENCE IN MICROBLOGS||SYNOPSIS|
|TED021||QUANTIFYING POLITICAL LEANING FROM TWEETS, RETWEETS, AND RETWEETERS||SYNOPSIS|
|TED022||PRACTICAL APPROXIMATE K NEAREST NEIGHBOR QUERIES WITH LOCATION AND QUERY PRIVACY||SYNOPSIS|
|TED023||SURVEY ON VIGILANCE OF INSTANT MESSAGES INSOCIAL NETWORKS USING TEXT MININGTECHNIQUES AND ONTOLOGY||SYNOPSIS|
|TED024||A NOVEL PIPELINE APPROACH FOR EFFICIENT BIG DATA BROADCASTINGENTITY LINKING WITH A KNOWLEDGE BASE: ISSUES, TECHNIQUES, AND SOLUTIONS||SYNOPSIS|
|TED025||ENTITY LINKING WITH A KNOWLEDGE BASE ISSUES TECHNIQUES AND SOLUTIONS||SYNOPSIS|
|TED026||SECURE DISTRIBUTED DEDUPLICATION SYSTEMS WITH IMPROVED RELIABILITY||SYNOPSIS|
|TED027||LEARNING TO RANK IMAGE TAGS WITH LIMITED TRAINING EXAMPLES||SYNOPSIS|
|TED028||A META-TOP-DOWN METHOD FOR LARGE-SCALE HIERARCHICAL CLASSIFICATION||SYNOPSIS|
|TED029||SECURE DISTRIBUTED DEDUPLICATION SYSTEMS WITH IMPROVED RELIABILITY||SYNOPSIS|
|TED030||ADVANCE MINING OF TEMPORAL HIGH UTILITY ITEMSET||SYNOPSIS|
|TED031||SLICING A NEW APPROACH TO PRIVACY PRESERVING DATA PUBLISHING||SYNOPSIS|
|TED032||FAST NEAREST NEIGHBOR SEARCH WITH KEYWORDS||SYNOPSIS|
|TED034||FAST DATA RETRIEVAL FROM DATABASES BEYOND THE DATABASE DIMENSIONS||SYNOPSIS|
|TED035||C-TREND: TEMPORAL CLUSTER GRAPHS FOR IDENTIFYING AND VISUALIZING TRENDS IN MULTI ATTRIBUTE TRANSACTIONAL DATA||SYNOPSIS|
|TED036||A SIGNATURE-BASED INDEXING METHOD FOR EFFICIENT CONTENTBASED RETRIEVAL OF RELATIVETEMPORAL PATTERNS||SYNOPSIS|
|TED037||HIDING SENSITIVE ASSOCIATION RULES WITH LIMITED SIDE EFFECTS EVENTUAL CLUSTERER: A MODULAR APPROACH TO DESIGNING HIERARCHICAL CONSENSUS||SYNOPSIS|
Datamining is very broad area related to database. Here is a list of projects related to data mining which are developed using latest techniques and algorithms. Latest Data Mining topics, Latest Data Mining Concepts for Diploma, Latest Data Mining Concepts for Engineering, Latest Data Mining Concepts for M-Tech, Data Mining project centers in Bangalore with high quality Training and development, Latest J2EE Projects with recent Technology.Here is a list of project ideas for Data Mining concepts.
Students belonging to third year mini projects or final year projects can use these projects as mini-projects as well as mega-projects. If you have questions regarding these projects feel free to contct us. You may also ask for abstract of a project idea that you have or want to work on.The own projects idea for diploma and Engineering students can also be done here.
IEEE 2017 WEB MINING/DATA MINING BASED PROJECTS
Data mining is the process of searching huge amount of data from different aspects and summarize it to useful information. Data mining is logical than physical subset. Our concerns usually implicate mining and text based classification on Data mining projects for Students.
The usages of variety of tools associated to data analysis for identifying relationships in data are the process for data mining. Our concern support data mining projects for IT and CSE students to carry out their academic research projects.
Technics used for Data Mining
- Anomaly detection (outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation.
- Association rule learning (dependency modelling) – Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.
- Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.
- Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam".
- Regression – attempts to find a function which models the data with the least error that is, for estimating the relationships among data or datasets.
- Summarization – providing a more compact representation of the data set, including visualization and report generation.
Data Mining Operations
- Link Analysis links between individuals rather than characterising whole
- Predictive Modelling (supervised learning) use observations to learn to predict
- Database Segmentation (unsupervised learning) partition data into similar groups.