In past, we had a question, what happens? But with data mining’ we discover that what will happen and why? (Nakasima et.al 2018 14 ). Data mining integrates the various technologies like statistics, machine learning and databases(Irshad et.al, 2018 15 ). It has applications in different disciplines like medical, financial, defence, intelligence and so on(Sohail et.al, 2019 8 ). The tools of data mining include clustering, classifications, associations and detections (Muhammad et.al, 2017 16 ). From decades, data mining have developed in many ways regarding techniques, which includes extracting association, neural networks, logic programming, rough sets and decision trees(Zhu et.al, 2018 17 ). Furthermore, data mining has gone beyond limits like the relational databases to the text mining and multimedia data(Ristoski et.al 2018 18 ); also it’s involved in the information security and detections(Santis et.al, 2018 19 ). After so many developments, companies are still facing some challenges like Scalability (Sohail et.al, 2017 8 ), but till far data mining is working on the massive quality of datasets and also engaged in working for the Terabyte sizes(Najjar et.al, 2018 20 ). By the enormous growth of data in different disciplines’ the question arise, Can this technology fulfilthe needs in extraction of Petabyte size data? This comes in the limitation and domination of data mining technology (on which this paper is focusing). As data mining involves some algorithms, it is important to understand the limitation of data mining algorithms and tools. Which requires the time and space for the complexity? In example: can these algorithms be completed in time? If the problem is decided, what is the complexity? For future predictions, we need to find out more about the complexity of markets and business(Alonso et.al, 2018 11 ) and how data mining is shining in the financial platforms.
The book is organized in three parts. Part One lays the foundation. Chapter 1 discusses the importance of determining the goal or clearly defining the objective from a business perspective. Chapter 2 discusses and provides numerous cases for laying the foundation. This includes gathering the data or creating the modeling data set. Part Two details each step in the model development process through the use of a case study. Chapters 3 through 7 cover the steps for data cleanup, variable reduction and transformation, model processing, validation, and implementation. Part Three offers a series of case studies that detail the key steps in the data modeling process for a variety of objectives, including profiling, response, risk, churn, and lifetime value for the insurance, banking, telecommunications, and catalog industries. As the book progresses through the steps of model development, I include suitable contributions from a few industry experts who I consider to be pioneers in the field of data mining. The contributions range from alternative perspectives on a subject such as multi-collinearity to additional approaches for building lifetime value models.
In this way, small datasets will be cross-validated many times, but larger ones may only be once. Although the search algorithm is independent of the specific supervised classifier used within its wrapper approach, in our set of experiments we will use the well-known Naive-Bayes (NB) (Cestnik, 1990) supervised classifier. This is a simple and fast classifier which uses Bayes rule to predict the class for each test instance, assuming that features are independent of each other given the class. Due to its simplicity and fast induction, it is commonly used on Data Mining tasks of high dimensionality (Kohavi & John, 1997; Mladenic, 1998). The probability of discrete features is estimated from data using maximum likelihood estimation and applying the Laplace correction. A normal distribution is assumed to estimate the class conditional probabilities for continuous attributes. Unknown values in the test instance are skipped. Despite its simplicity and its independence assumption among variables, the literature shows that the NB classifier gives remarkably high accuracy in many domains (Langley & Sage, 1994), and especially in medical ones. Despite its good scaling with irrelevant features, NB can improve its accuracy level by discarding correlated or redundant features. Because of its independence assump- tion of features to predict the class, NB is degraded by correlated features which violate this independence assumption. Thus, FSS can also play a ‘normalization’ role that discards these groups of correlated features, and ideally selects just one of them in the final model.
Students at Waikato have played a significant role in the development of the project. Many of them are in the above list of Weka contributors, but they have also contributed in other ways. In the early days, Jamie Littin worked on ripple-down rules and relational learning. Brent Martin explored instance-based learning and nested instance-based representations, Murray Fife slaved over relational learning, and Nadeeka Madapathage investigated the use of functional languages for express- ing machine learning algorithms. More recently, Kathryn Hempstalk worked on one-class learning and her research informs part of Section 7.5; likewise, Richard Kirkby’s research on data streams informs Section 9.3. Some of the exercises in Chapter 17 were devised by Gabi Schmidberger, Richard Kirkby, and Geoff Holmes. Other graduate students have influenced us in numerous ways, particularly Gordon Paynter, YingYing Wen, and Zane Bray, who have worked with us on text mining, and Quan Sun and Xiaofeng Yu. Colleagues Steve Jones and Malika Mahoui have also made far-reaching contributions to these and other machine learning projects. We have also learned much from our many visiting students from Freiburg, including Nils Weidmann.
At this point, we have provided some reviews of possible data mining and mining patterns applica- tions in the biological contexts, and we are ready to focus on the main subject of this chapter, that is, mining patterns in biosequences. A survey on approaches and algorithms used for the automatic discovery of patterns in biosequences is presented in Brazma et al. (1998a). In that work, a formula- tion of the problem of automatically discovering patterns from a set of sequences is given, where patterns with the expressive power in the class of regular languages are considered among those frequently used in molecular bioinformatics. That paper focuses on families, which are, groups of biologically related sequences, and two different but related problems of learning family descrip- tions thereof are described. The first problem considered is how to find a classifier function for a family of biosequences; this is a function that takes a sequence as an argument, and returns true over the members of the family, and false over nonmembers. The second problem is how to extract a description of conserved features in (i.e., characterizing) the family, expressed by a conservation function. Several solution spaces are discussed, illustrating different ways of de- fining such functions w.r.t. different biological problems, and the issue of ranking the solution space of discovered patterns is also discussed. Then, an in-depth review of algorithms used to find classification or conservation functions for sets of biosequences is given. The perspective put forward in Brazma et al. (1998a) highlights how the problem of pattern discovery in biosequences can be related to problems studied in the field of
Data Mining Methods and Models culminates in a detailed case study, Modeling Response to Direct Mail Marketing. Here the reader has the opportunity to see how everything that he or she has learned is brought all together to create actionable and proﬁtable solutions. The case study includes over 50 pages of graphical, exploratory data analysis, predictive modeling, and customer proﬁling, and offers different so- lutions, depending on the requisites of the client. The models are evaluated using a custom-built cost/beneﬁt table, reﬂecting the true costs of classiﬁcation errors rather than the usual methods, such as overall error rate. Thus, the analyst can compare models using the estimated proﬁt per customer contacted, and can predict how much money the models will earn based on the number of customers contacted.
A project has been deﬁned as any piece of work that is undertaken or attempted. Consequently, project management involves “the application of knowledge, skills, tools and techniques to a broad range of activities to meet the requirements of the particular project” . Project management is needed to organize the process of development and to produce a project plan. The way the process is going to be developed (life cycle) and how it will be split into phases and tasks (process model), will be established. This project deﬁn- ition  exactly describes the common understanding, its extent and nature, among the key people involved in a project. Thus, any data mining project need to be deﬁned to state the parties, goals, data and human resources, tasks, schedules, expected results, that comply the foundation upon which a successful project will be built. In general, any engineering project iterates through the following stages between inception and implementation: Justiﬁ- cation and motivation of the project, Planning and Business Analysis, Design, Construction, Deployment. In fact in software engineering this approach has been successfully applied. Although a data mining project has components similar to those found in IT, the nature is diﬀerent even some that concepts need to be modiﬁed in order to be integrated.
We rst examine the information-theoretic approach applied to the analysis of attribute relevance. Let's take ID3 as an example. ID3 constructs a decision tree based on a given set of data tuples, or training objects , where the class label of each tuple is known. The decision tree can then be used to classify objects for which the class label is not known. To build the tree, ID3 uses a measure known as information gain to rank each attribute. The attribute with the highest information gain is considered the most discriminating attribute of the given set. A tree node is constructed to represent a test on the attribute. Branches are grown from the test node according to each of the possible values of the attribute, and the given training objects are partitioned accordingly. In general, a node containing objects which all belong to the same class becomes a leaf node and is labeled with the class. The procedure is repeated recursively on each non-leaf partition of objects, until no more leaves can be created. This attribute selection process minimizes the expected number of tests to classify an object. When performing descriptive mining, we can use the information gain measure to perform relevance analysis, as we shall show below.
SOMs are an important methodology for descriptive data mining and they represent a valid alternative to clustering methods. They are closely related to non-hierarchical clustering algorithms, such as the k-means method. The fun- damental difference between the two methodologies is that SOM algorithms introduce a topological dependence between clusters. This can be extremely important when it is fundamental to preserve the topological order among the input vectors and the clusters. This is what happens in image analysis, where it is necessary to preserve a notion of spatial correlation between the pixels of the image. Clustering methods may overcentralise, since the mutual indepen- dence of the different groups leads to only one centroid being modiﬁed, leaving the centroids of the other clusters unchanged; this means that one group gets bigger and bigger while the other groups remain relatively empty. But if the neighbourhood of every neuron is so small as to contain only one output neu- ron, the Kohonen maps will behave analogously to the k-means algorithm. For a practical comparison of descriptive clustering algorithms, in Chapter 9 compares Kohonen networks with the k-means non-hierarchical clustering method.
Section 4.1 deals with the important concepts of proximity and distance between statistical observations, which is the foundation for many of the methods discussed in the chapter. Section 4.2 deals with clustering methods, the aim of which is to classify observations into homogeneous groups. Clustering is prob- ably the best known descriptive data mining method. In Section 4.3 we present linear regression from a non-probabilistic viewpoint. This is the most important prediction method for continuous variables. We will present the probability aspects of linear regression in Section 4.11. In Section 4.4 we examine, again from a non-probabilistic viewpoint, the main prediction method for qualitative variables: logistic regression. Another important predictive methodology is represented by tree models, which can be used both for regression and clustering purposes. These are presented in Section 4.5. Concerning clustering, there is a fundamental difference between cluster analysis, on the one hand, and logistic regression and tree models, on the other. In the latter case, the clustering is supervised, that is, measured against a reference variable (target or response), whose values are known. The former case, in contrast, is unsupervised: there are no reference variables, and the clustering analysis determines the nature and the number of groups and allocates the observations in them. In Sections 4.6 and 4.7 we introduce two further classes of predictive models, neural networks and nearest-neighbour models. Then in Section 4.8 we describe two very important local data mining methods: association and sequence rules.
The data interpretation stage is very critical. It assimilates knowledge from mined data. Two issues are essential. One is how to recognize the business value from knowledge patterns discovered in the data mining stage. Another issue is which visualization tool should be used to show the data mining re- sults. Determining the business value from discovered knowledge patterns is similar to playing “puzzles.” The mined data is a puzzle that needs to be put together for a business purpose. This operation depends on the interaction between data analysts, business analysts and decision makers (such as man- agers or CEOs). Because data analysts may not be fully aware of the purpose of the data mining goal or objective, and while business analysts may not understand the results of sophisticated mathematical solutions, interaction between them is necessary. In order to properly interpret knowledge pat- terns, it’s important to choose an appropriate visualization tool. Many visu- alization packages and tools are available, including pie charts, histograms, box plots, scatter plots, and distributions. Good interpretation leads to pro- ductive business decisions, while poor interpretation analysis may miss use- ful information. Normally, the simpler the graphical interpretation, the easier it is for end users to understand.
inside a Corpus. On the Document three operations are executed: tokenization and lemmatization, which are descript in second section, and mining rules application. To have a good tokenization and lemmatization it is important to get a clean and correct text by pre-processing. The result of Pre processing phase is stored in a database. The Text Mining phase, the prototype accedes to the database and draws out the URL and the description of job adverts. An analysis is done on description by a tool called GATE through API Java. The text contained in the description is identified as Document inside a Corpus. On the Document three operations are executed: tokenization and lemmatization, which are descript in second section, and mining rules application. To have a good tokenization and lemmatization it is important to get a clean and correct text by pre-processing. After tokenization and lemmatization, the application of mining rules to the Document is executed. It is very important to apply the rules in the correct sequence that is suitable in external file called main.jape as shown in Fig. 11. Changing the rules sequence it is gotten different result. The rules are based on keywords lists referred to precise universe, like the keywords city list contains only city names. The rule execution consists in a matching between the tokenized Document and the rule reference lists. If the token is present in more lists, the rule analyzes the lemmatized text and decides. The rules are stored in external files with jape extension like city.jape shown in Fig. 12. This rule, called City, individualizes cities which are contained in a text through a matching on all tokens of the same document through the list. This list is called citta.The matching result is stored in Annotators in Document in which there are information on the individualized tokens.
The research paper provides an analysis and comparison of key research efforts relating to workflow analysis and business process-mining. All process models are constructed and presented in workflow systems as graphical representation, so that it is clearly understood by all stakeholders. The business process is controlled by workflow management. The management of workflow deals with the automated coordination control and work as required by satisfying workflow processes. Generating a work ow design is a difficult time-consuming process and in general there are discrepancies between the actual work ow processes and the processes as perceived by the management. Process mining is extremely useful tool for managers and system administrators, who want to get an overview of task behavior and monitor the progress. The process models can be analyzed to gain insight into reality. Process mining techniques help to understand what is actually going on in reality and if it is what is actually desired. Current paper depicts the concepts of workflow management with the help of workflow log utilizing the process model, show casing exciting analytical results for workflow processes. The workflow cannot create the analyzed model with good fitness, so process mining tools are used to give a better overview of the business process for complicated workflow logs. Hence process mining concepts and tools are successful technological tools to aid in business process applications.
Older day’s web system mainly deals large collection of documents whereas recently documents always linked or embedded with lot of collection of multimedia documents with different types of files, heterogeneous data such as images, videos, audios. Millions of web pages are added every day and million of others are modified or deleted. The main aim of web content mining is knowledge discovery. While retrieval of data is difficult so that we need to find different new algorithms used to improve the system and also recent days we are using multilevel data bases , multidimensional data bases and web query systems. Most of the popular algorithms are used only numeric, character, text data types. The use of multimedia data is complicated or not suitable for current system, all the above most of the proposed algorithms are not fulfilling the requirements for web technology. All present web pages are Dynamic nature so data base cannot be assumed to be static. Most of the previous Content management support algorithms more flexible to static, while we use dynamic it’s require new suitable algorithm. The traditional way of retrieving images from data bases is to assign text annotations to image data. Presently we are using different types of data bases like Web site image database, web site text data base, web site video data base, web site image text data bases. On this finding or improving new filtering approaches is necessary to improve web multimedia meta data bases otherwise it leads poor interpretability of mining result.
Opencast mining activities cause severe changes to the landscape. Overburden dumps are man-made habitat causing multifarious environmental problems ranging from erosion and enhancing sediment load in receiving water bodies, dust pollution, damage to visual & aesthetics, fragmentation of habitat and overall disturbance of ecosystem in the entire area. The magnitude of ecological impacts depends upon existing ecological setting of the area where mining activities are taking place. Sediments deposited in layers in flood plains or terrestrial ecosystems can produce many impacts associated with surface waters, ground water, and terrestrial ecosystems. Minerals associated with deposited sediments may depress the pH of surface runoff thereby mobilizing heavy metals that can infiltrate into the surrounding subsoil or can be carried away to nearby surface waters. The associated impacts could include substantial pH depression or metals loading to surface waters and/or persistent contamination of ground water sources. Contaminated sediments may also lower the pH of soils to the extent that vegetation and suitable habitat are lost (Barve, 2011).
A telephone company must ensure that a high percentage of all phone calls are made within a certain period of time. Since each phone call must be routed through many switches, it is imperative that each switch work correctly. The failure of any switch could result in a call not being completed or being completed in an unacceptably long period of time. In this environment, a potential data mining problem would be to predict a failure of a node. Then, when the node is predicted to fail, measures can be taken by the phone company to route all calls around the node and replace the switch. To this end, the company keeps a history of calls through a switch. Each call history indicates the success or failure of the switch, associated timing, and error indication. The history contains results of the last and prior traffic through the switch. A transaction of the type (success, failure) indicates that the most recent call could not be handled successfully, while the call before that was handled fine. Another transaction (ERRl , failure) indicates that the previous call was handled but an error occurred, ERRl . This error could be something like excessive time. The data mining problem can then be stated as finding association rules of the type X ::::} Failure. If these types of rules occur with a high confidence, we could predict failure and immediately take the node off-line. Even though the support might be low because the X condition does not frequently occur, most often when it occurs, the node fails with the next traffic.
This brings us to direct marketing, another popular domain for data mining. Promotional offers are expensive and have an extremely low—but highly profit- able—response rate. Any technique that allows a promotional mailout to be more tightly focused, achieving the same or nearly the same response from a much smaller sample, is valuable. Commercially available databases containing demo- graphic information based on ZIP codes that characterize the associated neigh- borhood can be correlated with information on existing customers to find a socioeconomic model that predicts what kind of people will turn out to be actual customers. This model can then be used on information gained in response to an initial mailout, where people send back a response card or call an 800 number for more information, to predict likely future customers. Direct mail companies have the advantage over shopping mall retailers of having complete purchasing histories for each individual customer and can use data mining to determine those likely to respond to special offers. Targeted campaigns are cheaper than mass- marketed campaigns because companies save money by sending offers only to
Initial studies of allele mining have focused only on the identiﬁcation of SNPs/InDels at coding sequences or exons of the gene, since these variations were expected to affect the encoded protein structure and/or function. Ample examples are available to demonstrate the effect of such sequence variations in genic regions in altering the phenotypes. However, recent reports indicate that the nucleotide changes in non-coding regions (5′ UTR) including promoter, introns and 3′ UTR) also have signiﬁcant effect on transcript synthesis and accumulation which in turn alter the trait expression. Role of intronic mutations in gene regulation was evident in the expression of some genes like tubulin (components of microtubules) (Fiume et al., 2004) and rubi3 (polyubiquitin gene) (Samadder et al., 2008) in rice as well VRN-1 (which affect vernalization response) in barley and wheat (Fu et al., 2005). A mutation in 5′ splice site of the ﬁrst intron of the waxy (Wx) gene had resulted in tenfold increase in the gene activity in rice (Isshiki et al., 1998).
b). Behaviour scoring/credit rating migration analysis. Valuation of a customer‘s or product’s probability of a change in risk level within a given time.(i.e., default rate volatility) In commercial lending, risk assessment is usually an attempt to quantify the risk of loss to the lender when making a particular lending decision. Here credit risk can quantify by the changes of value of a credit product or of a whole credit customer portfolio, which is based on change in the instrument’s ranting, the default probability, and recovery rate of the instrument in case of default. Further diversification effects influence the result on a por tfolio level. Thus a major par t of implementation and care of credit risk management system will be a typical data mining problem: the modelling of the credit instrument’s value through the default probabilities, rating migrations, and recovery rates.
Challenges of Pattern Analysis are to filter uninteresting information and to visualize and interpret the interesting patterns to the user. First delete the less significance rules or models from the interested model storehouse; Next use technology of OLAP and so on to carry on the comprehensive mining and analysis; Once more, let discovered data or knowledge be visible; Finally, provide the characteristic service to the electronic commerce website.