Data mining concepts and techniques 2nd edition ebook

 
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  1. Data mining : concepts and techniques
  2. Data Mining: Concepts and Techniques (2nd edition)
  3. Data Mining: Concepts and Techniques - PDF Free Download
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Data Mining: Concepts and Techniques (2nd edition) Jiawei Han and Micheline Kamber Morgan Kaufmann Publishers, Bibliographic Notes for Chapter 2. Data Mining: Concepts and Techniques (2nd edition). Jiawei Han and Micheline Kamber. Morgan Kaufmann Publishers, Bibliographic Notes for Chapter. Information Modeling and Relational Databases, 2nd Edition. Terry Halpin . Data mining: concepts and techniques / Jiawei Han, Micheline Kamber, Jian Pei.

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Data Mining Concepts And Techniques 2nd Edition Ebook

Request PDF on ResearchGate | On Jan 1, , Jiawei Han and others published Data Mining Concepts and Techniques (2nd Edition). Data Mining: Concepts and Techniques This content was uploaded by our users and we assume good faith they have the permission to share this book. Data Mining: Concepts and Techniques (2nd edition) Jiawei Han and Micheline Kamber Morgan Kaufmann Publishers, Bibliographic Notes for Chapter 8.

There have been extensive studies on stream data management and the processing of continuous queries in stream data. For a description of synopsis data structures for stream data, see Gibbons and Matias [GM98]. Vitter introduced the notion of reservoir sampling as a way to select an unbiased random sample of n elements without replacement from a larger ordered set of size N, where N is unknown [Vit85]. A one-pass summary method for processing approximate aggregate queries using wavelets was proposed by Gilbert, Kotidis, Muthukrishnan, and Strauss [GKMS01]. Statstream, a statistical method for the monitoring of thousands of data streams in real time, was developed by Zhu and Shasha [ZS02, SZ04]. There are also many stream data projects. Examples include Aurora by Zdonik, Cetintemel, Cherniack, et al. A restricted subset of SQL was used as the query language in order to provide guarantees about efficient evaluation and append-only query results. A multidimensional stream cube model was proposed by Chen, Dong, Han, et al. For mining frequent items and itemsets on stream data, Manku and Motwani proposed sticky sampling and lossy counting algorithms for approximate frequency counts over data streams [MM02]. Karp, Papadimitriou and Shenker proposed a counting algorithm for finding frequent elements in data streams [KPS03]. Giannella, Han, Pei, et al.

ACM Comput. Guyon, N. Matic, and V. Discoverying informative patterns and data cleaning. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. A dynamic index structure for spatial searching. Han and Y. Dynamic generation and refinement of concept hierarchies for knowledge discovery in databases. Harinarayan, A. Rajaraman, and J. Implementing data cubes efficiently. The World According to Wavelets. Peters, Classification Algorithms. John and P. Static versus dynamic sampling for data mining.

Johnson and D. Applied Multivariate Statistical Analysis 5th ed. Prentice Hall, Discretization of numeric attributes. Kohavi and G. Wrappers for feature subset selection. Artificial Intelligence, L Kennedy, Y.

Lee, B. Van Roy, C. Reed, and R. Kivinen and H. The power of sampling in knowledge discovery. Liu and H. Motoda eds. Feature Extraction, Construction, and Selection: A Data Mining Per- spective. Liu, F. Hussain, C. Tan, and M.

An enabling technique. Data Mining and Knowledge Discovery, 6: Enterprise Knowledge Management: The Data Quality Approach. Morgan Kaufmann, Liu and R. Feature selection and discretization of numeric attributes. Langley, H. Simon, G. Bradshaw, and J.

Scientific Discovery: Computational Explorations of the Creative Processes. MIT Press, Muralikrishna and D. Equi-depth histograms for extimating selectivity factors for multi- dimensional queries. Neter, M.

Kutner, C. Nachtsheim, and L. Applied Linear Statistical Models 4th ed. Irwin, Data Quality: The Accuracy Dimension. Learning DNF by decision trees. Probabilistic Reasoning in Intelligent Systems. Morgan Kauffman, Poosala and Y. Selectivity estimation without the attribute value independence assump- tion.

Press, S. Teukolosky, W. Vetterling, and B. Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, Data Preparation for Data Mining. Unknown attribute values in induction. Programs for Machine Learning. Management and Technology. In the Aprioribased GSP algorithm, Srikant and Agrawal [SA96] generalized their earlier notion to include time constraints, a sliding time window, and user-defined taxonomies.

Zaki [Zak01] developed a vertical format-based sequential pattern mining method called SPADE, which is an extension of vertical-format-based frequent itemset mining methods, like Eclat and Charm [Zak98, ZH02]. PrefixSpan, a pattern growth approach to sequential pattern mining, and its predecessor, FreeSpan, were developed by Pei, Han, Mortazavi-Asl, et al.

The studies of sequential pattern mining have been extended in several different ways. Mannila, Toivonen, and Verkamo [MTV97] consider frequent episodes in sequences, where episodes are essentially acyclic graphs of events whose edges specify the temporal before-and-after relationship but without timing-interval restrictions. Garofalakis, Rastogi, and Shim [GRS99] proposed the use of regular expressions as a flexible constraint specification tool that enables usercontrolled focus to be incorporated into the sequential pattern mining process.

The embedding of multidimensional, multilevel information into a transformed sequence database for sequential pattern mining was proposed by Pinto, Han, Pei, et al. SeqIndex, efficient sequence indexing by frequent and discriminative analysis of sequential patterns, was studied by Cheng, Yan, and Han [CYH05].

Data mining for periodicity analysis has been an interesting theme in data mining. Lu, Han, and Feng [LHF98] proposed intertransaction association rules, which are implication rules whose two sides are totally ordered episodes with timing-interval restrictions on the events in the episodes and on the two sides. The notion of mining partial periodicity was first proposed by Han, Dong, and Yin, together with a max-subpattern hit set method [HDY99]. Ma and Hellerstein [MH01] proposed a method for mining partially periodic event patterns with unknown periods.

A general introduction can be found in Rabiner [Rab89]. Agrawal, C.

Data mining : concepts and techniques

Faloutsos, and A. Efficient similarity search in sequence databases. In Proc. Aggarwal, J. Han, J. Wang, and P. A framework for clustering evolving data streams. In Proc Int. A framework for projected clustering of high dimensional data streams.

On demand classification of data streams. Agrawal, K. Lin, H. Sawhney, and K. Fast similarity search in the presence of noise, scaling, and translation in time-series databases. Agrawal, G. Psaila, E. Wimmers, and M. Querying shapes of histories. Agrawal and R.

Data Mining: Concepts and Techniques (2nd edition)

Mining sequential patterns. Baldi and S. Bioinformatics: The Machine Learning Approach 2nd ed. Babcock, S. Babu, M. Datar, R. Motwani, and J. Models and issues in data stream systems. Brockwell and R. Introduction to Time Series and Forecasting 2nd ed. Springer, G.

Box, G. Jenkins, and G. Time Series Analysis: Forecasting and Control 3rd ed. Prentice-Hall, A. Baxevanis and B. Babu and J. Continuous queries over data streams. Bettini, X. Sean Wang, and S. Mining temporal relationships with multiple granularities in time sequences.

Cai, D. Clutter, G. Pape, J. Han, M. Welge, and L. Chen, G. Dong, J. Han, B. Wah, and J. Multi-dimensional regression analysis of timeseries data streams. Chandrasekaran and M. Streaming queries over streaming data. Cong, J. Han, and D. Parallel mining of closed sequential patterns. Cheng, X. Yan, and J. IncSpan: Incremental mining of sequential patterns in large database. Seqindex: Indexing sequences by sequential pattern analysis. Durbin, S.

Data Mining: Concepts and Techniques - PDF Free Download

Eddy, A. Krogh, and G. Cambridge University Press, A. Dobra, M. Garofalakis, J. Gehrke, and R. Processing complex aggregate queries over data streams. Success at large-scale transformation demands more than the best strategic and tactical plans, the traditional focus of senior executives.

A second theme is that electronic commerce has become a vital strategic-management tool. Note: The fourth and last strategy in the table below is not one of those. Uploaded by. This change management complete PowerPoint slideshow comprises of content-ready templates Each slide is well crafted and designed by our PowerPoint experts.

Change management is an extensive field as it touches on many aspects of organisational management including psychology, behavioural studies, general resource management and project management techniques. The Water Babies Wikipedia Tutorial - ppt download.

Change Management helps a project team ensure successful delivery of the business case. Participants learn powerful techniques on how to plan for change and manage resistance to change.

Class 1 Notes - Change Management. When the establishment of an intranet was suddenly feasible to any large organization, IT and management scientists declared the beginning of the quot;knowledge societyquot;. Learning principles in change management. Management of Strategic Change. PowerPoint PresentationTotal.

Processes, Organizations, and Information Systems - ppt video online Chapter 2 ppt download. Developed by the. Some teaching takes place in the M- are trained on Organizational Change Management. Supply chain management is undoubtedly one of those new and well grown management approaches emerged and rapidly developed across all industries around the world.

Effective change processes require a systemic view of the organization 6. Find notes, summaries, exercises for studying Change Management! Conclusion 4: Results-based management has been misinterpreted as not supporting. SlideTeam has come up with content ready leadership PowerPoint presentation slides to portray the management abilities of the workforce.

Substantive modification in some part of the organization ; It may include any aspect in the organization ; Work schedules ; Bases for departmentalization ; Span of management ; Organizational design ; Staff.

Today s In your opinion, what could we study in a course of change management? Change Management. Emotional Cycle of Change 3. Lecture notes, lectures - Noter.

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The functions of office management in brief, are given below: I. You may use this website for access to PPTs, guided notes, and make up with everyone the lecture notes on the subject Environmental management. A project Project team and change management teams are tracking progress and able to plan has been integrated with a change management plan.

Dont show me this again. Why Do Brainstorm post-it notes. Management - change lecture notes. Office management is similar to the general or administrative management; it performs the same functions as are performed by the management. An increasing number of companies are gaining competitive advantage by using the Internet for direct selling and for communication with suppliers, customers, creditors, partners, shareholders, clients, and competitors who also may be dispersed globally.

Share as much as you can about what is likely to occur if the change does not happen. Ch 4 Change Management Adapted from Prosci Some teaching takes place in the M-lecture notes on management and organizational behaviour mba i year i semester jntua-r15 mr. Change from within the workforce. Change denotes to cope development of touching from an unacceptable present state to a preferred state Beckhard and Dyer, The approaches include a combination of pressure tactics and coordination instead of competition and cooption as well as cooperation.

Below are my personal lecture notes from a Microsoft Project Level 1 class. Stemming from the view of change management as an area of professional practice there arises yet a third definition of change management: the content or subject matter of change management. Ability to change depends on employees. Once you have your map you can quickly and easily identify whats working well good practice and not so well areas for improvement.

These changes occur due to internal issues of company or advancement of technology. They will learn how to influence, persuade, coach and mentor in a change process.

The job of managing is likely to become more and more challenging in the 21st century for a number of reasons — rapid growth of the service sector, foreign competition, large number of corporate mergers and acquisitions, restructurings, business process 2 Change starts at the top and begins on day one: Change is inherently unsettling for people at all levels of an organization, and when it is on the horizon all eyes will turn to the CEO and the leadership team for strength, support, and direction.

It requires an intimate understanding of the human side, as well — the companys culture, values, people, and behaviorsContemporary Trends In Change Management Lecture Series One of the most unique and rewarding experiences available to students in the Master of Science in Management and Organizational Behavior program is an invitation to attend the two-day Contemporary Trends In Change Management lecture series on Benedictines campus.

Why change fails, and how to make your changes succeed. Aug 6, In this lesson, well be looking at strategic change management, which is a process of managing a change within an organization or company.

Our new CrystalGraphics Chart and Diagram Slides for PowerPoint is a collection of over impressively designed data-driven chart and editable diagram s guaranteed to impress any audience. A lecture on change management theories. Change affects the business and the individual. The concept of strategy The concept of strategy in business has been borrowed from military science and sports where it implies out- maneuvering the opponent.

Phase 2 Managing change Phase 1 Preparing for change Define your change management strategy Prepare your change management team Develop your sponsorship model Develop change management plans Take action and implement plans Collect and analyze feedback Diagnose gaps and manage readiness Implement actions and celebrate successes Phase 3 Do check out the sample questions of Change Management Lecture 01 for Management , the answers and examples explain the meaning of chapter in the best manner.

Jick and M. This deck comprises total of 25 change management PPT templates. The change management model involves ensuring four conditions are in place, which will drive the desired behavioral change of an initiative. This is one of over 2, courses on OCW.

Influence, Power, and Politics; Managing Conflict; Note: menu of options available to you in an organizational setting. Change management is the formal process for organizational change, including a Note that the three circled obstacles, are those that you, as a leader, can.

Branch Change management, which falls within the broader theoretical framework of social change Lewin , , has been a p. Environment notes cover ecology, bio- diversity, climate change and many latest. These team management PPT templates include slides like leadership introduction, leadership vs. A note on terminology: many researchers distinguish between primary and. Lack of executive-level support 3.

As humans we are not very good at changing.

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