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Data Mining Approaches

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Clustering Algorithms in Data mining

Genetic algorithms for multi-criterion classification and clustering in data mining
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ABSTRACT This paper focuses on multi-criteria tasks such as classification and clustering in the context of data mining. The cost functions like rule interestingness, predictive accuracy and comprehensibility associated with rule mining tasks can be treated as multiple

A survey on different clustering algorithms in data mining technique
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ABSTRACT Fast retrieval of the relevant information from databases has always been a significant issue. There are many techniques are developed for this purpose; In among data clustering is one of the major technique. The process of creating vital information from a

A Study On Spatial Data Clustering Algorithms In Data Mining
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Abstract The enormous amount of hidden data in large databases has produced incredible interests in the area of data mining. Clustering is an indispensable task in data mining to cluster data into significant subsets to obtain useful information. Clustering spatial data is

Analysis and Comparison of Efficient Techniques of Clustering Algorithms in Data Mining
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ABSTRACT This paper presents the comparison of data mining algorithms for clustering. These algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the

Forecasting Diabetes Mellitus Using Different Clustering Algorithms in Data Mining
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ABSTRACT Data Mining is the process of extracting information from large data sets through algorithms and techniques drawn from the field of Statistics, machine Learning and Data Base Management Systems. There are different kinds of data mining techniques are

A Comparative Study of Various Clustering Algorithms in Data Mining
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ABSTRACT Data mining is the process of extracting Knowledge from data. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. Clustering is one of the

A Synthesized Approach for Comparison and Enhancement of Clustering Algorithms in Data Mining for Improving Feature Quality
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ABSTRACT K-Means and Kohonen SOM clustering are two major analytical tools for unsupervised forest datasets. However, both have their innate disadvantages. Clustering is currently one of the most crucial techniques for dealing with massive amount of

An Analysis on Clustering Algorithms in Data Mining
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ABSTRACT Clustering is the grouping together of similar data items into clusters. Clustering analysis is one of the main analytical methods in data mining; the method of clustering algorithm will influence the clustering results directly. This paper discusses the various

An Experimental Analysis of Clustering Algorithms in Data Mining using Weka Tool
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ABSTRACT Cluster analysis divides data into meaningful or useful groups (clusters). It is a process for discovering groups and identifying interesting patterns. There are different types of clusters: Well-separated clusters, Center-based clusters, Contiguous clusters, Density-

PERFORMANCE ANALYSIS OF CLUSTERING ALGORITHMS IN DATA MINING IN WEKA
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ABSTRACT Data mining is the extraction of intriguing (relevant, constructive, previously unexplored and substantially valuable) patterns or information from huge stack of data. In other words, it is the exploration of links, associations and overall patterns that prevail in

A Survey on the Clustering Algorithms in Sales Data Mining
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ABSTRACT This paper discusses different clustering techniques that can be used in sales databases. The advancement of digital data collection and build up of data in data banks as a result of modernization in sales disciplines has brought in great challenges of data

Numerical Data Clustering Algorithms in Mining Real Estate Listings
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Abstract In the paper, we propose a method for mining real-estate listings using clustering algorithms intended for numerical data. The presented approach is based on information systems over ontological graphs. Such information systems have been proposed to deal

Clustering Algorithms Applied in Educational Data Mining
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ABSTRACT Fifty years ago there were just a handful of universities across the globe that could provide for specialized educational courses. Today Universities are generating not only graduates but also massive amounts of data from their systems. So the question that

Finding similar case subset and hotspot detection in felonious data set using data mining algorithms: Weighted Clustering and classification
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Abstract Felonious activities have been increased terrifically. So safety has become a key issue to the societies. The control activities should be taken by the Security agencies (like FBI, CBI, CID, police Department and other law enforcement agencies). The security

stream data mining

Stream Data Mining.
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ABSTRACT: Data mining is a part of a process called KDD-knowledge discovery in databases. This process consists basically of steps that are performed before carrying out data mining, such as data selection, data cleaning, pre-processing, and data Spatial statistics can be used to determine optimum areas for marketing goods and services. Analyzing current distributions of retail stores, for example, is important in determining locations of new stores and services. Spatial statistics can also be used to identify clusters

A survey of stream data mining
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Abstract At present a growing number of applications that generate massive streams of data need intelligent data processing and online analysis. Real-time surveillance systems, telecommunication systems, sensor networks and other dynamic environments are such

Online adaptation in learning classifier systems: stream data mining
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Abstract In data mining, concept drift refers to the phenomenon that the underlying model (or concept) is changing over time. The aim of this paper is twofold. First, we propose a fundamental characterization and quantification of different types of concept drift. The

Extension of Decision Tree Algorithm for Stream Data Mining Using Real Data
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Abstract:Recently, because of increasing amount of data in the society, data stream mining targeting large scale data has attracted attention. The data mining is a technology of discovery new knowledge and patterns from the massive amounts of data, and what the

A Wireless Data Stream Mining Model.
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Abstract. The sensor networks, web click stream and astronomical applications generate a continuous flow of data streams. Most likely data streams are generated in a wireless environment. These data streams challenge our ability to store and process them in real-

A holistic approach for resource-aware adaptive data stream mining
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Abstract Mining data streams is a field of increasing interest due to the importance of its applications and dissemination of data stream sources. Most of the streaming techniques developed so far have not addressed the need for resource-aware computing in data

Cbdt: A concept based approach to data stream mining
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Abstract. Data Stream mining presents unique challenges compared to traditional mining on a random sample drawn from a stationary statistical distribution. Data from real-world data streams are subject to concept drift due to changes that take place continuously in the

Mining concept-drifting data stream to detect peer to peer botnet traffic
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ABSTRACT We propose a novel stream data classification technique to detect Peer to Peer botnet. Botnet traffic can be considered as stream data having two important properties: infinite length and drifting concept. Thus, stream data classification technique is more

Sensory Stream Data Mining on Chip
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Abstract Mining physical properties from real-time sensor stream data is important to the atmospheric studies, ecology and oceanography. An FPGA-based reconfigurable sensory stream data mining processor is presented in this paper. The processor is based on

Real-time clinical decision support system with data stream mining
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This research aims to describe a new design of data stream mining system that can analyze medical data stream and make real-time prediction. The motivation of the research is due to a growing concern of combining software technology and medical functions for the

Using GNUsmail to Compare Data Stream Mining Methods for On-line Email Classification.
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Abstract Real-time classification of emails is a challenging task because of its online nature, and also because email streams are subject to concept drift. Identifying email spam, where only two different labels or classes are defined (spam or not spam), has received great

„Mobile Visualization for Sensory Data Stream Mining
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ABSTRACT With the emergence of ubiquitous data mining and recent advances in mobile communications, there is a need for visualization techniques to enhance the user- interactions, realtime decision making and comprehension of the results of mining

State-of-the-art in data stream mining
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Data streams became ubiquitous as many sources produce data continuously and rapidly. Examples of streaming data include customer click streams, telephone records, web logs, multimedia data, and sets of retail chain transactions. Data streams have brought new

Data stream mining
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Abstract-In recent years, so-called data streams have attracted considerable attention in different fields of computer science such as distributed systems, database systems, and data mining. A data stream can roughly be thought of as an ordered sequence of data items,

Fast Feature Selection for Naive Bayes Classification in Data Stream Mining
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Abstract-Stream mining is the process of mining a continuous, ordered sequence of data items in real-time. Naïve Bayes (NB) classification is one of the popular classification methods for stream mining because it is an incremental classification method whose

Stream mining on envronmental data
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ABSTRACT This paper addresses a problem of environmental data analysis, where the data is obtained from different data sources including sensor measurements. We propose a framework for stream mining on environmental data and illustrate its applicability on real-

Systems and techniques for distributed and stream data mining
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Abstract Nowadays huge amounts of electronic data are naturally collected in distributed sites, due to either plural ownership or geographical distribution of the processes that produce data. Moving data to a location for extracting useful and actionable knowledge is

Distributed and stream data mining algorithms for frequent pattern discovery
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Abstract The use of distributed systems is continuously spreading in several applications domains. Extracting valuable knowledge from raw data produced by distributed parties, in order to produce a unified global model, may presents various challenges related to either

Survival analysis meets data stream mining
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Abstract. Survival analysis deals with monitoring entities over their lifetime. The definition of" birth" and" death" events depends on the nature of a given entity. When we observe an infinite stream of birth and death events, at each point in time some of the monitored

Compact Tree for Associative Classification of Data Stream Mining
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Abstract The data streams have recently emerged to address the problems of continuous data. Mining with data streams is the process of extracting knowledge structures from continuous, rapid data records [1]. An important goal in data stream mining is generation

Data MiningData Stream Mining–Open Source Tools
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Abstract: Term of data mining was available in mid of 1990's. Previously offered through shell script, command line argument, filtering, pipeline. Today its awkward. Now days no of tools are developed by research community in data mining. They provides GUI interface

Mining click-stream data with statistical and rule-based methods
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Abstract. We present an analysis of the click-stream data provided for the ECML/PKDD data mining challenge. We primarily focus on predicting the next page that will be visited by a user based on a history of visited pages. We compare results of one statistical and two

Data and Event Stream Mining
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Summary Management and analysis of streaming data has become crucial with its applications on web and for transactions data. Data streams consist of mostly numeric data but what is more interesting are the events derived from the numerical data that need to be

Data Stream Mining
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Abstract The domain of stream data mining poses many new challenges compared to more traditional data mining applications. Large volumes of data over long periods of time render common mining and clustering techniques unusable. This paper will give the reader an

Data Stream Mining Using Landmark Stream Model for Offline Data Streams: A Case Study of Health Care Unit.
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ABSTRACT There exists much different kind of applications in data stream mining. Few main areas of applications include sensor networks, internet packet streams, web logs, medical data and many more. Data streams typically arrive in high speeds and change in data

Action prediction of opponents in MMORPG using data stream mining approach with heuristic motions
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ABSTRACT Millions of people now participate in massively multi player online role playing games (MMORPGs), placing tremendous and often unpredictable maintenance burdens on their operators. Thus, understanding the dynamic nature of MMORPGs players is critical

Preprocessing For Click Stream Data Mining
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ABSTRACT Web mining involves a wide range of applications that aims at discovering and extracting hidden information in data stored on the Web. The focus of this paper is to provide an overview of data preparation techniques and algorithms that can be used in order to

Analytical Study of Data Mining on Data Stream Using Skewed Distribution of Data
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Abstract:Analytical Study of Data Mining is an important data analysis tool that uses a model from historical data to predict class labels for new observations. More and more applications are featuring data streams, rather than finite stored data sets, which are a

Mining the Data Stream.
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Presentation to Hybrid Intelligent Systems 16 December 2003

Frequent Pattern Mining Over Data Stream Using Compact Sliding Window TreeSliding Window Model
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Abstract-The frequent pattern mining in data streams is more challenging than mining the patterns in traditional databases since several requirements need to be additionally satisfied. In the sliding-window model of data streams, transactions both enter into and

Peer-to-Peer Distributed Data Stream Mining and Monitoring
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Abstract: Distributed data mining (DDM) deals with the problem of analyzing distributed, possibly multi-party data by paying attention to the computing, communication, storage, and human factors-related issues in a distributed environment. Unlike the conventional off-the-

New Sampling and Sketching Techniques for Data Stream Mining
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Abstract The information and telecommunications revolution in the last decades results in the generation of massive amounts of data. This abundance of information offers the opportunity to obtain a more profound knowledge about the complex world we live in.

Geochemical Trace-element Data for Stream Sediment Samples Collected in the
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Accuracy Extended Ensemble–A Brood Purposive Stream Data mining
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Abstract:The objective of Data mining is to haul out knowledge from gigantic quantity of data. The storage, querying and mining of such data sets are highly computationally challenging tasks. Mining data streams is concerned with extracting knowledge structures

Structured Stream Data Mining Using SVM Method as Basic Classifier
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Abstract Recently, the huge number of email spams has caused serious problems in essential email communication. In this paper, we describe the results of an empirical study on one spam detection method namely Support Vector Machines (SVMs). To conduct the

Clustering for Context Inference in the Data Stream Mining
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Abstract In an environment in which several events are sensed in a complex manner and sequentially obtained, a clue can be obtained for inference of situations by classifying each event and analyzing the aspect of change of each event. The study proposes a method to

Stream Data Mining and Anytime Algorithms
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Abstract. Sensors pervade all areas of personal, environmental and industrial domains, and nearly all applications in engineering, telecommunication, business, and life sciences produce tremendously increasing amounts of data. Though the availability of storage

Approximate Maximal Frequent Mining over Data Stream
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Abstract Maximal frequent

CSPMDS-PREFIXSPAN: MINING CLOSED SEQUENTIAL PATTERNS OVER DATA STREAMSLIDING WINDOWS
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Abstract. Previous studies indicate incremental methods were not used sufficiently and data structures always consumed large memories and long execution time because of lack of pruning method in data stream algorithms. It has shown that obtaining closed patterns

request paperMap-reduce merge: simplified relational data processing on large clusters,

Map-reduce-merge: simplified relational data processing on large clusters
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Abstract Map-Reduce is a programming model that enables easy development of scalable parallel applications to process a vast amount of data on large clusters of commodity machines. Through a simple interface with two functions, map and reduce, this model

data mining for automated personality classification

Lexical predictors of personality type
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Automated coreference resolution, for example, is a very difficult unsolved problemWe have described a method for classifying texts by personality type of the authorWorkshop on Text Mining, ACM International Conference on Knowledge Discovery and DataMining, Boston, MA

Mining facebook data for predictive personality modeling
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Datamining techniques play a fundamental role in extract- ing correlation patterns between datasets may require considera- tion of additional features, more sophisticated data pro- cessing Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and

Decision support systems and intelligent systems
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Through Data Integration and Planning 301 Case Application 5.3 Cluster Analysis for DataMining 302 CHAPTERAcquisition from Experts 583 11.5 Knowledge Acquisition from Multiple Experts 595 11.6 Automated Knowledge Acquisition from Data and Documents

Too neurotic, not too friendly: Structured personality classification on textual data
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Using linguistic cues for the automatic recognition of personality in conversation and text. Classifying author personality from weblog text. DataMining: Practi- cal Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems

Words mark the nerds: Computational models of personality recognition through language
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We evaluated the automatic tagger by applying it to a set of 100 hand labeled utterances randomly selected in the EAR corpus.Interactive assessment of user preference models: the automated travel assistant. DataMining: Practical machine learning tools and techniques.

Toward Personality Insights from Language Exploration in Social Media.
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In Proceedings of the ninth international conference on Knowledge discovery and datamining, 475– 480. Blei, DM; Ng, AY; and Jordan, MI 2003. Latent dirichlet allocation.Large scale personalityclassification of bloggers. Automatic recognition of personality in conversation.

Tales of tuning-prototyping for automatic classification of emotional user states.
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Anyway, it seems to be promising to use automaticclassification procedures for such screening 4, 7], interaction background, multi-modal features [8], and personality traits.On comparing classifiers: Pitfalls to avoid and a recommended approach, DataMining and Knowledge

Role of data mining in education sector
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Cios et al. (2007) indicates that datamining allows for automated discovery of patterns and trends in the data. Tsai et al. Data is mined with the help of Cubist tool. They conclude that datamining has an enormous application in higher education.

Personality traits recognition on social network-facebook
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To make these automated systems more human-like, we need to understand human behavior and how it is affected byUsing linguistic cues for the automatic recognition of personality in conversation and text. DataMining: Practical Machine Learning Tools and Techniques.

Trainable Generation of Big-Five Personality Styles through Data-Driven Parameter Estimation.
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It might be possible to improve the parameter estimation models with a larger sample of random utterances at development time, or with additional extreme data generated using theUsing linguistic cues for the automatic recognition of personality in conversation

Adaptive game AI
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Automatic creation of strong knowledge bases Automatic determination of priority values Opponent Models in Video GamesModel typesImplicitTP Immediately classified correctly NUS

Data mining and neural networks from a commercial perspective
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Knowing a person's age is one of the first steps in developing a personality for the customer. Neural networks performance can be highly automated, minimizing human involvement.There is rarely one right tool to use in datamining; it is a question as to what is available and

Mining a clinical data warehouse to discover disease-finding associations using co-occurrence statistics.
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5]. Since the 1990s, co-occurrence statistics have also been widely used in synonym mining and wordstatistics can be effectively used to find disease-finding associations hidden in a clinical data warehouse Automated medical problem list generation: Towards a patient timeline

Workshop on computational personality recognition (shared task)
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Mining Authors' Personality Traits from Modern Greek Spontaneous Text.Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text.

PERSONAGE: Personality generation for dialogue
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To control the level of implic- itness, the SUBJECT IMPLICITNESS parameter deter- mines whether predicates describing restaurant atThe automated design of believable dialogues for animated presentation teams DataMining: Practical ma- chine learning tools and techniques

Social media profiling: A Panopticon or Omniopticon tool
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Council of Europe Recommendation CM/Rec(2010)13 of the Committee of Ministers to member states on the protection of individuals with regard to automatic processing of personal data in theSchermer, B.: The limits of privacy in automated profiling and datamining.

Conceptual framework of data mining process in management education in India: an institutional perspective
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any, ranging from a minimum of six months), extra curricular activities, over all personality and severalimpetus of prevalent advanced applications like Predicting student performances and success of programs, automated alerts and what if analysis using datamining will be

Automatic detection of gender and handedness from on-line handwriting
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Automatic Detection of Gender and Handedness from On-Line Handwritingfrom digit ratio and sex role identity as determinants of the sex of handwriting. Personality and individualA Tutorial on Support Vector Machines for Pattern Recognition. DataMining and Knowledge

Comparative study of speaker personality traits recognition in conversational and broadcast news speech.
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To make these automated systems more like human[3] F. Mairesse, MA and RK Moore, Using linguistic cues for the automatic recognition of personality in conversation

Ensemble methods for personality recognition
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Mairesse, F.; Walker, MA; Mehl, MR; and Moore, RK 2007. Using linguistic cues for the automatic recognition of personality in conversation and text.Ensemble methods in datamining: improving accuracy through combining predictions.

• research paper-computer science-data mining
• Temporal pattern mining
• Pocket data mining-towards collaborative data mining in mobile computing environments
• bio-data mining
• data mining in wireless networks

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