data mining research papers


data mining heuristic



Customer segmentation of bank based on data mining-security value based heuristic approach as a replacement to k-means segmentation
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ABSTRACT K-means segmentation algorithm can be applied to Customer Segmentation in Banks. If loan over-due amount of bank customers are normally distributed, then K-means can be used. In cases of significant outliers, K-means segmentation algorithm cannot be

A Hybrid Meta Heuristic Algorithm for Discovering Classification Rule in Data Mining
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Abstract The Meta heuristics provide acceptable solutions in a reasonable time for solving hard and complex problems in medical, science and engineering. In this paper we present a meta heuristic based technique for mining rule over a medical data base were the use of

Data Mining: an Aid Towards more Efficient Hyper-heuristic Search
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Hyper-heuristics is an emerging search technology motivated by the goal of raising the level of generality at which optimisation systems can work (Burke et al. 2003). A hyper-heuristic can be thought of as a process which uses a higher level search methodology to choose

An appropriate pattern to solving a parallel machine scheduling by a combination of meta-heuristic and data mining
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Abstract: Scheduling problems' applications in nowadays competitive world and the rate of their result's usage in industrial area demonstrate the importance of this problem. In this article, an identical parallel machine by the objective function of total weighted tardiness is

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 multiplayer 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

Data Mining Heuristic-Based Malware Detection for Android
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Page 1. DATAMININGHEURISTIC-BASED MALWARE DETECTION FOR ANDROID APPLICATIONS by Naser Peiravianiv ABSTRACT Author: Naser Peiravian Title: DataMining Heuristic-Based Malware Detection for Android Applications Institution: Florida Atlantic University

Privacy Preserving Data Mining Using Heuristic Approach
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Abstract Data mining is the process of identifying patterns from large amount of data. Association rule mining aims to discover dependency relationships across attributes. It may also disclose sensitive information. With extensive application of data mining techniques

A Research on Privacy Preserving Data Mining Using Heuristic Approach
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Abstract:Data mining is the process of identifying patterns from large amount of data. Association rule mining aims to discover dependency relationships across attributes. It may also disclose sensitive information. With extensive application of data mining techniques

TEMPERATUTE PREDICTION USING HEURISTIC DATA MINING ON TWO-FACTOR FUZZY TIME-SERIES
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ABSTRACT The Data mining technology is widely used in various fields. Temperature forecasting is also the domain area where data mining is used. Accuracy is always an issue in forecasting and it will remain a matter of the concern forever. This paper presents data

A data mining approach to performance refinement of a heuristic algorithm
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Abstract--This paper introduces a novel approach to performance refinement of a heuristic algorithm for combinatorial optimization. The proposed Adaptive Tabu Search (A–TS) algorithm introduces adaptive behavior in the traditional Tabu Search algorithm. The

Addressing churn prediction problem with Meta-heuristic, Machine learning, Neural Network and data mining techniques: a case study of a telecommunication
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Abstract In order to survive in today telecommunication market it is essential to have the ability of distinguishing customers who are probable to switch into a competitor. Customer churn prediction is a means to address the complication and has become an important

Data mining: an overview from a database perspective
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the computational complexity of some data mining methods motivate the development of parallel form dedicated studies to invent new data mining methods or develop integrated techniques tional databases [33], [75] are also studied recently, with efficient algorithms developed.

Automatic subspace clustering of high dimensional data for data mining applications
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following spe- cial requirements on clustering techniques, motivating the need for developing new algorithms:How- ever, these techniques were developed to discover clusters in the full dimensional spacea main memory data structure (hash tree [l]) that allows efficient querying

E cient and E ective Clustering Methods for Spatial Data Mining
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As for the efficiency concern, we develop our own cluster analysis algorithm, called CLARANS, which is designed forbecomes too costly for large values of n and k. This analysis motivates the development of CLARA.

Mining frequent patterns without candidate generation
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Second, an FP-tree-based pattern fragment growth miningmethod, is developed, which starts from a frequent length-1 pattern as anIn this section, we will study how to explore the compact information stored in an FP-tree and develop an efficientminingmethod for mining

BIRCH: an efficient data clustering method for very large databases
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Page 1. BIRCH: An EfficientData Clustering Method for Very Large Databases the dataset. Besides, the derived clusters can be visualized more efficiently and effectively than the original dataset[Lee81, D,J80].shove IO method wrt. efficiency In addition, it, may

CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets.
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an efficientalgorithm, CLOSET, for mining closed itemsets, with the develop- ment oftree structure for mining closed itemsets without candidate generation, (2) developing a single developed for this purpose: (1) the framework of a recently developedefficient frequent pattern

State-of-the-art in privacy preserving data mining
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privacy preserving datamining is the identification of suitable evaluation criteria and the development of relatedare different from the technique that a sanitization al- gorithm has been developed forAlternatively, we may need to develop a formal framework that upon testing of a

A distribution-based clustering algorithm for mining in large spatial databases
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Compared to former k-medoid algorithms, CLARANS is more effective and more efficient.To our best knowledge, CLARANS is the first clustering algorithm developed for spatial databases. Thus, the efficiency of DBCLASD on large spatial databases is very attractive when

Privacy-preserving data mining
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Since the primary task in data mining is the development of models about aggregated data, can tree classifiersand show that that it is possible to develop accurate models A decision tree classifier is developed in two phases: a growth phase and a prune phase

Data mining: concepts, models, methods, and algorithms
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field of datamining has made a good progress both in developing new methodoloThe new discipline of datamining has developed especially to extract valuable information fromapply a particular methodology, to understand a method's limitations, or to develop new techniques

Density-based clustering in spatial databases: The algorithm gdbscan and its applications
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Obviously the efficiency of the above algorithm depends on the efficiency of the neighborhood on spatial proximity like distance predicates or intersection which can be efficiently supported byindex structures are assumed to be available in a SDBS for efficient processing of

Meta-learning in distributed data mining systems: Issues and approaches
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Here, we explore one of the main challenges in this research area, the development of techniques Financial institutiontargeted to their own assetEach site is developed on top of the JVM (Java Virtual Machine), with the

Towards on-line analytical mining in large databases
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Our early development of attribute-oriented induc- tion method 13] adopts two generalization help marketers discotargeted marketing considerations, a data cube-based, multi-level clustering module is being developed in

data mining papers



Total coliforms and data mining as a tool in water quality monitoring
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Total coliforms, as a microbiological indicator of water quality, have been tested on the basis of condition, dynamics, as well as on the dependence on other physicochemical and biological parameters, by methods and models of data mining. Using a combination of

Study and Evaluation of user s behavior in e-commerce Using Data Mining
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ABSTRACT Data mining has matured as a field of basic and applied research in computer science. The objective of this dissertation is to evaluate, propose and improve the use of some of the recent approaches, architectures and Web mining techniques (collecting

Creating Time-Varying Fuzzy Control Rules Based on Data Mining
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ABSTRACT In this study, we propose the method creating fuzzy control rules based on experiential knowledge. With the advent of data mining techniques, a new way is provided to create fuzzy control rules. With the introduction of incremental data mining techniques,

A study on time series data mining based on the concepts and principles of Chinese I-Ching
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This paper proposes a novel time series data mining and analysis framework inspired by ancient Chinese culture I-Ching. The proposed method converts the time series into symbol spaces by employing the concepts and principles of I-Ching. Algorithms are addressed to

Novel approaches of data-mining in experimental physics
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ABSTRACT. Data mining for processing experimental data in high energy and nuclear physics led to many multiparametric problems, two of them are considered:(i) hypothesis testing and classification approaches based on artificial neural networks and boosted

Knowledge Extraction In Hadith Using Data Mining Technique
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ABSTRACT Muslims believe that the Sunnah of the Prophet Muhammad (SAAW) is the second of the two revealed fundamental sources of Islam, after the Holy Qur an. Hadith can provide a Gold Standard ground truth for AI (Artificial Intelligent) knowledge extraction and

A FRAMEWORK FOR DATA MINING USER NAVIGATION PATTERNS
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ABSTRACT Data mining is the process of extracting hidden patterns from data. As more data is gathered, with the amount of data doubling every year, data mining is becoming an increasingly important tool to transform this data into information. In this paper, we present

An Efficient Mining Algorithm for Closed Frequent Itemsets and its Associated Data
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 [9] MJ Zaki and C.-J. Hsiao, Charm: An Efficient Algorithm for Closed Itemsets Mining, Proc. Second SIAM Int?l Conf. Data Mining, Apr. 2002. [10] Jiawei Han and Micheline Kamber, DATA

An Adaptive parameter free data mining approach for healthcare application
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ABSTRACT In today s world, healthcare is the most important factor affecting human life. Due to heavy work load it is not possible for personal healthcare. The proposed system acts as a preventive measure for determining whether a person is fit or unfit based on his/her

MASSIVE DATA MINING (MDM) ON DATA STREAMS USING CLASSIFICATION ALGORITHMS
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ABSTRACT Data sets which continuously and rapidly grow over time are referred to as data streams. Few examples include network monitoring, sensor data, browsing world wide web, usage of credit cards. The advances in information technology have lead the large flows of

Using data mining to enhance library services and collections
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Summary The bibliography describes many different uses of data mining within libraries. Studies have enhanced library collections and library services. The goal is for the bibliography to serve as introduction to good ideas and ways to enhance users

What about sequential data mining techniques to identify linguistic patterns for stylistics?
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In this paper, we study the use of data mining techniques for stylistic analysis, from a linguistic point of view, by considering emerging sequential patterns. First, we show that mining sequential patterns of words with gap constraints gives new relevant linguistic

Utilization of Data mining Approaches for Prediction of Life Threatening Diseases Survivability
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ABSTRACT Data mining now-a-days plays an important role in prediction of diseases in health care industry. The Health care industry utilizes data mining Techniques and finds out the information which is hidden in the data set. Many diagnoses have been done for

A Genetic Algorithm for Discovering Classification Rules in Data Mining
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ABSTRACT Data mining has as goal to discover knowledge from huge volume of data. Rule mining is considered as one of the usable mining method in order to obtain valuable knowledge from stored data on database systems. In this paper, a genetic algorithm-

A Combined Model of MCDM and Data Mining for Determining Question Weights in Scientific Exams
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ABSTRACT The existence of an appropriate system for assessing educational development is a key issue in achieving the pre-determined educational goals. The abundance of papers in recent years which investigate the necessity and adequacy of evaluation corroborate this

Reconsidering the Multiple Criteria Decision Making Problems of Construction Projects; Using Advanced Visualization and Data Mining Tools
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The effectiveness and performance of the interactive visualizations are evaluated along with a number of study cases related to construction workers . Along with our study cases the aspects of data mining, modeling, and visualization the data related to construction

Frequent Pattern Mining in Social Network Data (Facebook Application Data)
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 comAbstract Data Mining is a process, which involves identification of useful patterns and gathers knowledge which was hidden earlier. It  attention. One of the emerging application areas of Data Mining is Social Networks. In

Methods of Logic Synthesis and Their Application in Data Mining
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In practice, logic synthesis methods are mainly used to process the digital systems which have been designed to transform binary signals. The primary task of these methods is to optimize the implementation of the systems for different technology mapping. However, it

Improvement of Mining Fuzzy Multiple-Level Association Rules from Quantitative Data
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 IranABSTRACT Data-mining techniques have been developed to turn data into useful task-oriented knowledge. Most  level. Extracting multilevel association rules in transaction databases is most commonly used in data mining. This

A Survey on Privacy Preservation in Distributed Data mining for Unstructured Data Environment
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ABSTRACT Unstructured data can be textual or non-textual. Textual unstructured data are generated in media like email messages, power point presentation, word document, text files. Non-textual unstructured data are generated in media like JPEG images, MP3audio

Contribution to the Creation Of DMX Queries in Mining Student Data
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 ABSTRACT. The paper is a contribution to creating DMX queries for mining student data. The focus is placed on the possibilities of data mining techniques applied in e-learning. DMX queries are created and applied against data mining models.

Experimental Data Mining Techniques (Using Multiple Statistical Methods)
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Abstract This paper discusses the possible solutions of non-linear multivariable by experimental Data mining techniques using orthogonal array. Taguchi method is a very useful technique to reduce the time and cost of the experiment but ignores all kind of

Assessment of Micro Loan Payment using Structured Data Mining Techniques: The Case of Indonesian People Credit Bank
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Medium Enterprises (MSMEs) in Indonesia presents a challenge for small rural banks such as People Credit Banks. These banks are required to infer risks about customers loan repayment from structured (quantitative, financial) and unstructured (qualitative, non-

Rules Generation from ERP Database: A Successful Implementation of Data Mining
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ABSTRACT An Enterprise Resource Planning (ERP) applications has been remarkably adopting during the current era. It has become an vital factor for the success of business organizations. This paper represents the efficient implementation of data mining

CONTROL CHART AND DATA MINING METHODS IN THE ANALYSIS OF PROCESS DATA
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ABSTRACT The idea of finding patterns in process data is not new. Traditionally, in the pursuit focused on achieving the desired level of quality, which is primarily defined by the needs of customers, statistical process control is widely accepted methodological framework for

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