Quality Assessment in Data Mining

Data Mining is mainly concerned with methodologies for extracting patterns from large data repositories. There are many data mining methods which accomplishing a limited set of tasks produces a particular enumeration of patterns over data sets. The main tasks of data mining which have already been discussed in previous sections are: i) Clustering, ii) Classification, iii) Association Rule Extraction, iv) Time Series, v) Regression, and vi) Summarization.

[1]  Mohammed J. Zaki,et al.  CHARM: An Efficient Algorithm for Closed Itemset Mining , 2002, SDM.

[2]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[3]  Michalis Vazirgiannis,et al.  A Data Set Oriented Approach for Clustering Algorithm Selection , 2001, PKDD.

[4]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[5]  Laks V. S. Lakshmanan,et al.  Exploratory mining and pruning optimizations of constrained associations rules , 1998, SIGMOD '98.

[6]  Gerardo Beni,et al.  A Validity Measure for Fuzzy Clustering , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Maria E. Orlowska,et al.  CCAIIA: Clustering Categorial Attributed into Interseting Accociation Rules , 1998, PAKDD.

[8]  Padhraic Smyth,et al.  Clustering Using Monte Carlo Cross-Validation , 1996, KDD.

[9]  Boudewijn P. F. Lelieveldt,et al.  A new cluster validity index for the fuzzy c-mean , 1998, Pattern Recognit. Lett..

[10]  Nicolas Pasquier,et al.  Discovering Frequent Closed Itemsets for Association Rules , 1999, ICDT.

[11]  Yiyu Yao,et al.  Peculiarity Oriented Multi-database Mining , 1999, PKDD.

[12]  Michael J. A. Berry,et al.  Data mining techniques - for marketing, sales, and customer support , 1997, Wiley computer publishing.

[13]  J. Dunn Well-Separated Clusters and Optimal Fuzzy Partitions , 1974 .

[14]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[15]  Nikhil R. Pal,et al.  Cluster validation using graph theoretic concepts , 1997, Pattern Recognit..

[16]  Padhraic Smyth,et al.  Rule Induction Using Information Theory , 1991, Knowledge Discovery in Databases.

[17]  Jian Pei,et al.  CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets , 2000, ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery.

[18]  G. W. Snedecor Statistical Methods , 1964 .

[19]  Carlos Bento,et al.  A Metric for Selection of the Most Promising Rules , 1998, PKDD.

[20]  Hichem Frigui,et al.  Quadratic shell clustering algorithms and the detection of second-degree curves , 1993 .

[21]  T. F. O'Brien,et al.  WHONET: an information system for monitoring antimicrobial resistance. , 1995, Emerging infectious diseases.

[22]  Jian Pei,et al.  Can we push more constraints into frequent pattern mining? , 2000, KDD '00.

[23]  Rajeev Motwani,et al.  Dynamic itemset counting and implication rules for market basket data , 1997, SIGMOD '97.

[24]  Michalis Vazirgiannis,et al.  Clustering validity assessment: finding the optimal partitioning of a data set , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[25]  G. W. Milligan,et al.  An examination of procedures for determining the number of clusters in a data set , 1985 .

[26]  Hichem Frigui,et al.  The Fuzzy C Quadric Shell clustering algorithm and the detection of second-degree curves , 1993, Pattern Recognit. Lett..

[27]  Rajesh N. Davé,et al.  Validating fuzzy partitions obtained through c-shells clustering , 1996, Pattern Recognit. Lett..

[28]  Gregory Piatetsky-Shapiro,et al.  Discovery, Analysis, and Presentation of Strong Rules , 1991, Knowledge Discovery in Databases.

[29]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[30]  Wynne Hsu,et al.  Using General Impressions to Analyze Discovered Classification Rules , 1997, KDD.

[31]  Dimitrios Gunopulos,et al.  Constraint-Based Rule Mining in Large, Dense Databases , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[32]  Sudipto Guha,et al.  ROCK: A Robust Clustering Algorithm for Categorical Attributes , 2000, Inf. Syst..

[33]  Thomas G. Dietterich Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms , 1998, Neural Computation.

[34]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  Heikki Mannila,et al.  Finding interesting rules from large sets of discovered association rules , 1994, CIKM '94.

[36]  Subhash Sharma Applied multivariate techniques , 1995 .

[37]  Isak Gath,et al.  Unsupervised Optimal Fuzzy Clustering , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[38]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[39]  Michalis Vazirgiannis,et al.  Quality Scheme Assessment in the Clustering Process , 2000, PKDD.

[40]  Ramakrishnan Srikant,et al.  Mining Association Rules with Item Constraints , 1997, KDD.