# Data Minin

## Top PDF Data Minin:

### Advanced Data Mining Techniques Olson DS (2008) pdf

“\$20,000” to “\$100,000,” we can use the formula S = (x – min)/(max – min) to “shrink” any known salary value, say \$50,000 to 0.6, a number in [0.0, 1.0]. If the mean of salary is given as \$45,000, and standard deviation is given as \$15,000, the \$50,000 can be normalized as 0.33. Transforming data from the metric system (e.g., meter, kilometer) to English system (e.g., foot and mile) is another example. For categorical to numerical scales, we have to assign an appropriate numerical number to a categorical value according to needs. Categorical variables can be ordinal (such as less, moderate, and strong) and nominal (such as red, yellow, blue, and green). For example, a binary variable {yes, no} can be transformed into “1 = yes and 0 = no.” Note that transforming a numerical value to an ordi- nal value means transformation with order, while transforming to a nomi- nal value is a less rigid transformation. We need to be careful not to intro- duce more precision than is present in the original data. For instance, Likert scales often represent ordinal information with coded numbers (1–7, 1–5, and so on). However, these numbers usually don’t imply a common scale of difference. An object rated as 4 may not be meant to be twice as strong on some measure as an object rated as 2. Sometimes, we can apply values to represent a block of numbers or a range of categorical variables. For example, we may use “1” to represent the monetary values from “\$0” to “\$20,000,” and use “2” for “\$20,001–\$40,000,” and so on. We can use “0001” to represent “two-store house” and “0002” for “one-and-half-store house.” All kinds of “quick-and-dirty” methods could be used to transform data. There is no unique procedure and the only criterion is to transform the data for convenience of use during the data mining stage.

### Data Mining Techniques for Software Quality Prediction in Open Source Software - An Initial Assessment

Figure 2 describes our solution workflow. We have selected some online datasets (i.e., NASA, Eclipse, Android and Elastic Search), including all the metrics contained in those repositories. We have performed some cleaning operations when needed, replacing missing values with the mean of the other values related to the same metric [27]. The obtained data have been used as input of many data mining techniques (both supervised and unsupervised) by employing three different free open source tools: Weka [37], scikit learn [38] and R [39]. We have collected the output of all the executions of the algorithms and we have compared their values according to the performance indicators.

### Comparison of Several Data Mining Methods in Credit Card Default Prediction

LightGBM is an open-source, distributed and high-performance GB frame- work built by Microsoft company. LightGBM has some advantages such as fast learning speed, high parallelism efficiency and high-volume data, and so on. Based on the open data set of credit card in Taiwan, five data mining me- thods, Logistic regression, SVM, neural network, Xgboost and LightGBM, are compared in this paper. The results show that the AUC, F 1 -Score and the

### Applied Data Mining For Business And Industry Paolo Giudici (2009) pdf

All this suggests the need for a systematic study of how to compare and evaluate statistical models for data mining. In this chapter we will review the most important methods. As these criteria will be frequently used and compared in Part II of the text, this chapter will just offer a brief systematic summary without giving examples. We begin by introducing the concept of discrepancy for a statistical model; it will make us look further at comparison criteria based on statistical tests. Although this leads to a very rigorous methodology, it allows only a partial ordering of the models. Scoring functions are a less structured approach developed in the field of information theory. We explain how they give each model a score, which puts them into some kind of complete order. Another group of criteria has been developed in the machine learning field. We introduce the main computational criteria, such as cross-validation. These criteria have the advantage of being generally applicable but might be data-dependent and might require long computation times. We then introduce the very important concept of combining several models via model averaging, bagging and boosting. Finally, we introduce a group of criteria that are specifically tailored to business data

### Classification Algorithms in Data Mining – A Survey

Classification is a data mining technique that assigns categories to a collection of data in order to aide in more accurate predictions and analysis [2]. Also called sometimes called a Decision Tree, classification is one of several methods intended to make the analysis of very large data sets effective. To create an effective set of classification rules which answers a query, makes decision based on the query and predicts the behavior. To begin with a set of training data sets are created with certain set of attributes or outcomes.The main objective of the classification algorithm is to mine, how that set of attributes reaches its conclusion.

### Data mining for business intelligence with data integration

In general, data integration of multiple information systems aims at combining selected systems so that they form a unified form a new system and give users the vision of interacting with one single information system. The reason for integration is for two reasons. The first is given a set of existing information systems, an integrated view can be created to facilitate information access and reuse through a single information access point. And the second is given a certain information need, data from different complementing information systems is to be combined to gain a more comprehensive basis to satisfy the need. In the view of business intelligence context, the integration problem is commonly referred to as enterprise integration (EI). Enterprise integration denotes the capability to integrate information and functionalities from a variety of information systems in an enterprise. In this paper we are focusing on the challenges of data integration and finding the solution with the mining models for Enterprise Integration.

### Diabetes data prediction using data classification algorithm

significantly in the case of proposed CkNN method. In a research work of, Estebanez, Alter and Valls used genetic programming for classification tasks. The error rate for SVM is 22%, Simple Logistics is 22.14% and Multilayer perceptron is 23.31%. In another work some of the classification algorithms are compared by utilizing matrix and classification accuracy. The 10-fold cross validation method was used by three different types of breast cancer databases and calculated the accuracy. Jianchao Han used type 2 diabetes data for his effort and the decision tree using WEKA has been used to put up the prediction model. The main element for his research was predicting the disease is the models of Plasma Insulin. Asma A. Aljarullah in her research work J48 decision tree classifier was used. Using Diabetic data set was used to implement Association rule. B.M. Patil finds out different range of accuracies using some of classification techniques on the diabetes dataset. Weighted least squares support vector machine based on quantum particle swarm optimization algorithm is used to development in the prediction accuracy. E.G.Yildirim in his research work the type 2 diabetes data set is used to predictive data mining and applied in dosage planning. Adaptive Neuro Fuzzy Inference System and Rough Set theory methods are coated by him. The main objective of G. Parthiban et al. in their research work the prediction and changes of diabetic patient getting heart related problem. In their research they used Naive Bayes classifier method which gives the best possible prediction model.

### A Survey On Weighted Sentiment Analysis using Artificial Bee Colony Algorithm

As the size of digital information grows exponentially, large volumes of raw data need to be extracted. Nowadays, there are several methods to customize and manipulate data according to our needs. The most common method is to use Data Mining (DM). DM has been used in previous years for extracting implicit, valid, and potentially useful knowledge from large volumes of raw data (Sousa, Silva, & Neves). The extracted knowledge must be accurate, readable, comprehensible, and ease of understanding. Furthermore, the process of data mining is also called as the process of knowledge discovery which has been used in most new inter-disciplinary area such as database, artificial intelligence statistics, visualization, parallel computing and other fields. We found that many optimization algorithms have been used for classification tasks. From the best of our knowledge, previous researches on ABC algorithm have focused on optimization but none of them is for classification tasks [19]. The ABC algorithm is innovated in 2005 by Karaboga inspiring the social life of the bees to solve the optimization problems. This algorithm is a simulation of the food search of the group of the bees [22]. The group bees can be distributed in different distances to utilize the food resources. In ABC algorithm, the bees are classified in three groups:

### A Survey on Different Techniques for Mining Frequent Itemsets

Abstract— Data mining faces a lot of challenges in the big data era. Association rule mining algorithm is not sufficient to process large data sets. Apriori algorithm has limitations like the high I/O load and low performance. The FP-Growth algorithm also has certain limitations like less internal memory. Mining the frequent itemset in the dynamic scenarios is a challenging task. A parallelized approach using the mapreduce framework is also used to process large data sets. The various techniques for mining the frequent itemsets have been discussed.

### A Novel Frequent Subgraph Mining Based Intelligent MapReducing Technique

ABSTRACT: MapReducing is one of the most important task in Data Mining industry. It is essential to analyze the graph oriened data to manipulate the resulting sequences at every iteration over large data processing. Frequent Subgraph Mining (FSM) is implemented in this system to efficiently analyze the subgraph patterns with MapReducing concept clearly. This algorithm/methodology is more suitable for processing large data structure and mines the data with small amount of memory requirement to avoid cost consumptions. Be that as it may, as this present reality chart information develops, both in size and processing, such a suspicion does not hold any more. To defeat this, some diagram database-driven strategies have been proposed lately to solve FSM; notwithstanding, a disseminated arrangement utilizing MapReduce worldview has not been investigated broadly. Since MapReduce is getting to be the true worldview for calculation on monstrous information, an effective FSM calculation on this worldview is of immense request. In this system, an incessant subgraph mining calculation called FSM-H is proposed, which utilizes an iterative MapReduce based system. FSM-H is finish as it returns all the continuous subgraphs for a given client characterized support, and it is effective as it applies every one of the advancements that the most recent FSM calculations receive. For all our empirical results demonstrate our approach is more suitable to process large dataset in efficient manner without any cost and time wastage.

### Investigating User Ridership Sentiments for Bike Sharing Programs

Mention: Mention acknowledges a user with the symbolic “@” sign without using the “reply” feature. The Twitter handle of Capital Bikeshare is “bikeshare”. The tweets from the Capital Bikeshare timeline were collected for nine months (October 2013-June 2014). The Twitter handle was created in July of 2010. Nearly 6200 official tweets were made from this account between July of 2000 and February of 2015. The account has twelve thousand followers. It is important to note that the one-time tweet extraction limit from a Twitter handle is limited to 3200. Popular data mining “R” packages “twitter” and “tm” were used in this study to extract tweets for analysis [10] [11]. Twitter currently implements two forms of authentication in the new model, both still leveraging open standard for authorization (OAuth). These two forms are: 1) Application-user authentica- tion which is the most common form of resource authentication in Twitter’s OAuth 1.0A implementation to date. 2) Application-only which is a form of authentication where user application makes Application Programming Interface (API) requests on its own behalf, without a user context [12]. The collected tweets (count = 591), by using newly implemented Twitter OAuth, are distributed by hour of the day in Figure 2. Most of the tweets were made from 11 AM to 11 PM.

### A Prediction Model for Child Development Analysis using Naive Bayes and Decision Tree Fusion Technique – NB Tree

Finally, child development focuses on the ways people change and grow during their lives. It seeks in which areas and in what periods, people show change and growth and when and how their behaviour reveals consistency and continuity with prior behaviour.Some of the data mining technique used for child development analysis used machine learning algorithms such as Rough set apprach and Decision tree algorithm, Fuzzy expert systems, Neural Networks etc..[2,3,4].

### Frequent Itemset Mining Based on Differential Privacy using RElim (DP RElim)

designing differentially private data mining algorithms. Many researchers are working on design of data mining algorithms which gives differential privacy. In this paper, to explore the likelihood of planning a differentially private FIM , cannot just accomplish high information utility and a high level of protection, additionally offers high time effectiveness. To this end, the differentially private FIM based on the FP-growth algorithm, which is speak about to as PFP-growth. The Private RElim algorithmic program consists of a pre-processing part and a mining part. within the preprocessing part, to enhance the utility and privacy exchange, a completely unique good smart splitting technique is expected to rework the database.A frequent itemset miningwith differential privacy is important which will follow twophase process of pre-processing and mining. Through formal private investigation, demonstrate that our Private DP-RElim is "ɛ- differentially private. Broad analyses on genuine datasets show that our DP-RElim algorithm considerably outflanks the best in class systems.The computational experiments on real world and synthetic databases exhibit the fact that in comparison to the performance of previous algorithms, our algorithms are faster and also maintain high degree of privacy, high utility and high time efficiency simultaneously.

### Survey and Challenges of Mining Customer Data Sets to Enhance Customer Relationship Management

Analytical CRM is “Applying business analytics techniques and business intelligence such as data mining and online analytic processing to CRM applications” [ 2 ]. In other words analytical CRM is the interpretation of data collected by the operational CRM to identify the opportunities, optimize customer interaction, and manage business performance. Analytical CRM takes the data gathered from sources such as marketing campaigns, and products group then runs algorithms over it for analysis and interpretation purposes. Analytical CRM has been used to optimize profitability, revenue and customer satisfaction analysis, customer profiling and categorization, up/cross selling, fraud analysis and churn management. One of the most used tools for aCRM is data mining.

### An Efficient Approach of Association Rule Mining on Distributed Database Algorithm

Association rule mining is a significant management. The Optimized Distributed Association Mining Algorithm is worn for the mining progression distributed background. The reply time throughout the announcement and calculation factors are measured to conquer the finer arrival time, batch of processors in a single environment. As the mining process is ended in parallel an finest possible solution is obtained. The various graphs show the processing time as estimated and generate the results as per the requirements of the users. Fast response time as shown in the graphs shows that the proposed algorithm generates the outcome as needed. The upcoming improvement of this is to work about on proxy server to permit users to access new data searched even when the data is found in the neighborhood. The exploitation of conventional approach will be hard to collect the latest demand for data mining, so the new data mining algorithm proposed in this paper is meaningful. This paper increases data mining helpfulness significantly. This DB method can solve the algorithm space problem in our environment. The response time with the statement and calculation factors are measured to attain an enhanced response time. The routine analysis is completed by rising the quantity of processors in a distributed environment. As the mining course is done in similar an optimal resolution is obtained. The prospect enrichment of this is to collect the same dataset and uncover out the facts extracted out of that. A visual analysis can also be made for the same.

### Survey on-Big Data Analysis and Mining

The rise of Big Data is driven by the rapid increasing of complex data and their changes in volumes and in nature [6]. Documents posted on WWW servers, Internet backbones, social networks, communication networks, and transportation networks, and so on are all featured with complex data. While complex dependency structures underneath the data raise the difficulty for our learning systems, they also offer exciting opportunities that simple data representations are incapable of achieving. For example, researchers have successfully used Twitter, a well-known social networking site, to detect events such as earthquakes and major social activities, with nearly real time speed and very high accuracy. In addition, by summarizing the queries users submitted to the search engines, which are all over the world, it is now possible to build an early warning system for detecting fast spreading flu outbreaks . Making use of complex data is a major challenge for Big Data applications, because any two parties in a complex network are potentially interested to each other with a social connection. Such a connection is quadratic with respect to the number of nodes in the network, so a million node network may be subject to one trillion connections. For a large social network site, like Facebook, the number of active users has already reached 1 billion, and analyzing such an enormous network is a big challenge for Big Data mining. If we take daily user actions/interactions into consideration, the scale of difficulty will be even more astonishing.

### Mining Hot-Personae Approach Based on Local Social Microblog Graph

On the one hand, constructing the netizens’ online so- cial graphs and mining their focal areas from their in- ternet behavior are very important for user profiling. Graph mining approach based on the classic graph theory is a relatively new area of research in online social network microblog platform [28]. Yu et al. [44] showed that traditional data mining methods are not suitable for online social networks that do not pro- vide users’ preferences or rating data. However, link prediction is superior to other methods in online so- cial networks with sparse user characteristics, so this method is used to extract the missing information in many cases [25]. Link prediction methods based on graph theory are usually used to predict whether user will make a friend with anybody in the future. The computation using link prediction only needs to consider the users’ link relations, and detailed fea- tures are not required. Only the similarities among the nodes in the graphs should be considered [25, 30].

### Role of Knowledge Engineering in the Development of a Hybrid Knowledge Based Medical Information System for Atrial Fibrillation

In this paper, we are describing role of knowledge engineering in the development of a hybrid knowledge based medical information system. Knowledge engineering plays important role in development of various technologies such as: ex- pert systems, neural network, artificial intelligence, hybrid intelligent systems, data mining, decision support systems, and knowledge based systems etc. The hybrid medical information system mainly consists of medical information sys- tem and medical knowledge base systems. These poly techniques of knowledge engineering when integrated with hy- brid techniques of intelligent systems for designing, implementing knowledge bases to deal with medical informational data of Atrial Fibrillation. Atrial Fibrillation is the most common heart rhythm disorder which increases the risk of mortality and morbidity.