M. S. Chen, J. Han, P. S. Yu. "Data mining: An overview from a database perspective." IEEE Trans. on Knowledge and Data Eng., 8, 6, pp. 866--884, 1996.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....Our algorithms employ prior knowledge collected during the mining process and or via sampling, to further reduce the number of candidate itemsets and identify false candidate itemsets at an earlier stage. 1 Introduction Recently, data mining has attracted much attention in the database community [3, 4]. One of the most investigated topics in data mining is the problem of discovering association rules over basket data [1, 6, 8, 9] Basket data, which typically contains items purchased by a customer, are collected at the point of sales system in a retail business. Each basket data defines a ....

M. Chen, J. Han, and P. S. Yu. Data mining: An overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6):866--883, December 1996.

....values of l and m have been proposed [11, 23, 31] PPM is useful to nd patterns in sequences and particularly in hyperlink traversals. In contrast, our technique to generate prediction rules is aimed to nd semantic links among queries. Data Mining is about techniques for nding patterns in data [24, 2, 15, 28]. An important technique of data mining is the generation of association rules, which nd intra session patterns. Association rules were rst introduced in [3] and have motivated a considerable amount of work, as presented in [4] The algorithms presented in this paper are inspired from those ....

M. S. Chen, J. Han, and P. S. Yu. Data mining: An overview from database perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6):866{ 883, 1996.

....in scientific and industrial domains, generates massive amounts of data. This explosive growth in data and databases generates the need for new techniques and tools that can intelligently and automatically transform the data into useful information and knowledge. Data mining is such a technique [PF91, CHY96]. Data mining, which is also referred to as knowledge discovery in databases (KDD) means a process of nontrivial extraction of implicit, previous unknown, and potentially useful information (such as knowledge rules, constraints, and regularities) from data in databases [PF91] Various data ....

M. S. Chen, J. Han, and P. S. Yu, "Data Mining: An Overview from a Database Perspective," IEEE Transactions on Knowledge and Data Engineering, Vol. 8, No. 6, December 1996, pp. 866-883.

....database. Diverse data mining tools have been studied with various problems including neural networks, decision trees, rule induction, bayesian belief networks, evolutionary algorithms, fuzzy sets, association rules, and clustering, and their particular merits and demer its have been enumerated [2][4] A particular method is not the best in all situations and it is important to locate a method suitable to the problem. Genetic programming (GP) 11] is one of the search techniques that utilize the principle of natural evolution. In a GP, a solution is represented by a tree representing a ....

M. S. Chen and P.S. Han, J. Yu. Data mining: An overview from a database perspective. IEEE Transactions on Knowledge and Data Engineer- ing, 8(6):866-883, 1996.

....compare it with other concept lattice constructing methods [1] Discovering association rules among items in large databases is recognized as an important database mining problem. The problem was introduced in [7] for sale transaction databases. The problem can be formally described as following [6]. Let I = i 1 , i 2 , i m be a set of literals, called items. Let D be a set of transactions, where each transaction T is a set of items such that T I. The quantities of items bought are not considered. Each transaction is assigned an identifier, called TID. Let X be a set of items, A ....

Chen, M. S., Han, J., Yu, P. S.: Data Mining:An Overview from a Database Perspective. IEEE Trans. On Knowledge and Data Eng., 8(1996)

.... of sequence databases [2, 12] Similarity search is an operation that finds sequences or subsequences whose changing patterns are similar to that of a given query sequence [1, 2, 12] Similarity search is of growing importance in many new applications such as data mining and data warehousing [7, 22]. Similarity search is classified into whole matching and subsequence matching [1] Assuming that all the data and query sequences have the same length, whole matching searches for the data sequences similar to a query sequence. Subsequence matching searches for the subsequences, contained in data ....

....larger with the increasing length of data sequences. 6 Concluding Remarks Similarity search is an operation that finds data sequences whose changing patterns are similar to that of a query sequence [1, 12] and is of growing importance in such applications as data mining and data warehousing [7, 22]. Time warping is a useful transformation in such situations where the Euclidean distance is not applicable since the sequences to be compared are of di#erent lengths [19, 27] In this paper, we have discussed an e#cient approach for similarity search that supports the time warping ....

M. S. Chen, J. Han, P. S. Yu, "Data Mining: An Overview from Database Perspective", IEEE TKDE , Vol. 8, No. 6, pp. 866-883, 1996.

....which attributes or features to use for describing the concept; deciding how to combine such attributes to get the right concept induction. Because of that, the problem of feature selection is central to machine learning and to application of the field like data mining and knowledge discovery [34, 44]. As for statistical pattern recognition, the dimensionality reduction is essential for both complexity and accuracy issues; current machine learning application need algorithms able to scale up to real world problems and attaining high accuracy. As already noticed in the previous section, ....

J. Han M. Chen and P. Yu. Data mining: an overview from database perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6):866--833, 1996.

....pruned if and only if A is a descendent of B or B is a descendent of A. The rationale behind this heuristic is to avoid the numerous rules that would result in a deep inheritance hierarchy (e.g. if say A has many descendents B) Misleading Rules Some generalized association rules are misleading [2]. For example, suppose the rule x#y z has confidence 60 while the rule y z has confidence 80 . In that case, the first rule is misleading since the presence of x actually decreases the likelihood of finding the item z. More generally, given a y with confidence c , we say it is misleading ....

....given the presence of a particular rule, another rule may not be surprising to us. In that case, it is desirable to additionally prune the latter rule to focus the user s attention on those rules that are interesting. The pruning process that follows builds upon several existing techniques [2, 11]. Consider rules X y and rule X # y,whereX # is a subset of X . If we know the confidence c # for rule X # y, then we expect the confidence for rule X y to also be c # since there is no reason to believe without prior knowledge of the library and or applications that the ....

M. Chen, J. Han, and P. S. Yu. Data mining: An overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6):866-- 883, 1996.

....Computation Model to a Secure Multiparty Computation Model Single Input Computation Model Multi Input Computation Model D = union(D1, D2) A) Transformation of a Multi Input Computation Model (B) Transformation of a Single Input Computation Model Figure 8.1. Models example, in data mining [23] and statistical analysis, all the inputs usually come from one data set al..though the inputs consist of multiple data items. Next we want to transform both models to the Secure Multi party Computation model, in which the input from each participating party is considered as private. In certain ....

Ming-Syan Chen, Jiawei Han and Philip S. Yu. Data mining: an overview from a database perspective. IEEE Transactions On Knowledge and Data Engineering, 8:866-883, 1996.

....The BIRCH algorithm by Zhang et al. [12] introduced a similar concept of clustering features (CFs) to summarize non binary data in a cluster. Note that, although we use the term centroid based here, each a j is a cover for the documents within a give cluster, not a centroid (or even a medoid [13]) the use of the term centroid , which suggests a j is somehow in the middle of the clustered documents, may be misleading. In outline, a hierarchical agglomerative clustering method proceeds as follows [14] 1. Begin with n = p clusters, each containing one element; 2. Merge the pair of ....

M.-S. Chen, J. Han, and P. S. Yu, \Data mining: an overview from a database perspective," IEEE Transactions on Knowledge and Data Engineering, vol. 8, no. 6, pp. 866-883, Dec. 1996.

....we tested it using several real life databases. The experimental results show that it can be very effective at data mining tasks. In fact, when compared to popular data mining algorithms, our approach can be better able to uncover useful rules hidden in databases. 1. Introduction research [2, 9 12, 24, 26]. The problem is concerned with the mining of a set of production rules that can allow the values of an attribute in a database to be accurately predicted based on those of other attributes [1 2, 16, 19, 22, 24] For example, we are given a customer database with each record characterized by such ....

M.-S. Chen, J. Han, and P.S. Yu, "Data Mining: An Overview from a Database Perspective," IEEE Trans. on Knowledge and Data Engineering, vol. 8, no. 6, pp. 866883, 1996.

....of sources we gain access to a large amount of data that can again be used to apply Knowledge Discovery in Database (KDD) techniques and thus extract higher level information that might be useful. A simple scenario could be the following: based on the stored citation data we can build clusters [CHY96, FPSM92] by finding medoids that represent central objects. Those medoids are representative for a certain cluster of objects by having a minimal overall distance (citation steps) from objects within this cluster and on the other hand are most distinct from all other medoids found. Those clusters ....

M. Chen, J. Han, and P. S. Yu. Data mining: an overview from a database perspective. IEEE Trans. On Knowledge And Data Engineering, 8:866--883, December 1996.

....I has enormous time and space requirements. Many serial and parallel algorithms have been designed to tackle this problem. In the next chapter we will survey previous such work. Background In recent years, large quantities of data have been amassed with advances in data acquisition capabilities [16]. The domain of this data ranges from retail transactions [4] world wide web [20] and telecommunications[72] to astrophysics [61] stock market [39] biological databases [19, 62] weather, geological, environmental [24] and several others. There are virtually no limits to the kind of data that ....

.... in Databases (KDD) Process which consists of selection, pre processing, transformation, data mining, interpretation evaluation [25] Data Mining may be described as a process of nontrivial extraction of implicit, previously unknown and potentially useful information from data in databases [16]. Fayyad et al. de ne data mining as a step in the KDD process that consists of applying data analysis and discovery algorithms that, under acceptable computational eciency limitations, produce a particular enumeration of patterns (or models) over the data [27] KDD itself is referred to as ....

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M.-S. Chen, J. Han, and P. S. Yu. Data mining: an overview from a database perspective. IEEE Trans. On Knowledge And Data Engineering, 8:866-883, 1996.

....contradict the RUD, even if a majority of tuples in I supports the RUD. In data mining, one is generally not only interested in exact regularities, but also in strong regularities. To capture the strength of a RUD, we adapted the notion of confidence that is common in association rule mining [6]. The confidence of a RUD P Q is the conditional probability that two tuples t 1 ; t 2 , which are randomly selected from I without replacement, satisfy t 1 Q;U t 2 , given they already satisfy t 1 P;U t 2 . The task then is to mine RUDs that satisfy a certain threshold confidence. We have ....

M.-S. Chen, J. Han, and P. Yu. Data mining: An overview from a database perspective. IEEE Trans. on Knowledge and Data Engineering, 8(6):866--883, 1996.

....as cash registers in super markets or satellites in space, has increased quite dramatically. This trend makes it more and more difficult and time consuming to analyze those data collections for interesting information. Responding to this challenge, the field of knowledge discovery in databases [CHY96] has become a major focus of research. As result of this development many computerized data mining tools and environments have been proposed for the purpose of finding interesting patters in large data collections. However, each data mining technique makes assumptions with respect to composition ....

Chen, M-S., Han, J., and Yu, P.S. Data mining: An Overview from a database perspective. IEEE Transactions on knowledge and data engineering, Vol. 8, No. 6, Dec. 1996.

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Chen, M.-S., Han, J., and Yu, P. S. 1996. Data mining: an overview from a database perspective. Ieee Trans. On Knowledge And Data Engineering 8, 866--883.

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M. S. Chen, J. Han, and P. S. Yu, "Data Mining: An overview from a database perspective," IEEE Trans. Knowl. Data Eng., vol. 8, pp. 866--883, Dec. 1996.

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M. Chen, J. Han, and P. Yu. Data mining: an overview from database perspective. IEEE Trans. Knowledge and Data Engineering, 8(6):866--883, 1996. 44

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M.-S.Chen,J.Han,andP.S.Yu.DataMining:An Overview from Database Perspective. IEEE 8(6):866---883, December #996.

....databases. By knowledge discovery in databases, interesting knowledge, regularities, or high level information can be extracted from the relevant sets of data in databases and be investigated from different angles. Various data mining capabilities have been explored in the literature [1] 2][4][8] 9] 10] 16] It is noted that utilizing the technique of mining sequential patterns is able to extract valuable knowledge from the alarm data generated by a GSM system [11] 14] In mining sequential patterns, the input data is a set of sequences, called data sequences. Each data sequence is a ....

M-S. Chen, J. Han, and P. S. Yu.: Data Mining: An Overview from Database Perspective. IEEE Transactions on Knowledge and Data Engineering (December 1 996) (6):866---883.

....method. Keywords: Knowledge Discovery in Databases, Association Rule Mining, Evidence Theory, Uncertainty Reasoning, and Measure of Total Uncertainty. 1. INTRODUCTION Association rule mining is one of the most widely applied algorithms in knowledge discovery in databases (KDD) or data mining [4][7] 15] 16] Originally proposed by [2] its basic idea is to discover important and interesting associations among the data items such that the presence of some items in a transaction will imply the presence of other items in the same transaction. The outputs generated from the association rule ....

M.-S. Chen, J. Han, and P. S. Yu, "Data Mining: An Overview from Database Perspective," IEEE Transactions on Knowledge and Data Engineering, vol. 8, no. 6, pp. 866-883, 1996.

....is, in orders of magnitude, smaller than those required by the schemes which are directly extended from existing methods. 1 Introduction The discovery of association relationship among a huge database has been known to be useful in selective marketing, decision analysis, and business management [4, 9]. A popular area of applications is the market basket analysis, which studies the buying behaviors of customers by searching for sets of items that are frequently purchased together (or in sequence) For a given pair of confidence and support thresholds, the problem of mining association rules is ....

M.-S. Chen, J. Han, and P. S.Yu. Data Mining: An Overview from Database Perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6):866-- 883, December 1996.

....clustering results of very good quality. Keywords Data mining, clustering analysis, marketbasket data, small large ratios. 1 Introduction Mining of databases has attracted a growing amount of attention in database communities due to its wide applicability to improving marketing strategies [3][4]. Among others, data clustering is an important technique for exploratory data analysis [5] 6] In essence, clustering is meant to divide a set of data items into some proper groups in such a way that items in the same group are as similar to one another as possible. Market basket data analysis ....

M.-S. Chen, J. Han, and P. S. Yu. Data Mining: An Overview from a Database Perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6):866--833, 1996.

....citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific pelmission and or a fee. SIGKDD 02 Edmonton, Albela, Canada Copyright 2002 ACM 1 58113 567 X 02 0007 . 5.00. keting, decision support, and business management [3]. A popular area of applications is the market basket analysis, which studies the buying behaviors of customers by searching for sets of items that are frequently purchased either together or in sequence. Since the earlier work in [1] sev eral technologies on association rule mining have been ....

M.-S. Chen, J. Hah, and P. S.Yu. Data Mining: An Overview from Database Perspective. IEEE TKDE, 8(6):866-883, December 1996.

....and large water bodies and areas in USA. Multi level association rules can be mined by climbing up and stepping down along any of the two dimensions (name of water bodies or area names in the states relation) The detailed association rule mining algorithms have been studied by many researchers [2], and our spatial mining method is based on our study on the methods for mining spatial association rules [t0] For example, the following association rules may be derived from the spatial data set. is a(X, large BCtown ) close to(X, sea ) 40 , 52 ) is a(X, large BCtown ) close to(X, ....

M. S. Chen, J. Han, and P.S. Yu. Data mining: An overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering, 8:866-883, 1996.

....hierarchical clustering, partitional clustering 1. INTRODUCTION Data mining has attracted a significant amount of research attention due to its usefulness in many applications, including selective marketing, decision support, business manage ment, and user profile analysis, to name a few [3]. Among others, data clustering is one of the most active research areas [8] The data clustering techniques can be used to perform similarity search, pattern recognition, trend analy sis, grouping, classification, and so forth [3] In general, there are two types of attributes associated ....

.... business manage ment, and user profile analysis, to name a few [3] Among others, data clustering is one of the most active research areas [8] The data clustering techniques can be used to perform similarity search, pattern recognition, trend analy sis, grouping, classification, and so forth [3]. In general, there are two types of attributes associated Pelmission to make digital or hard copies of all or prat of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this ....

M.-S. Chen, J. Hah, and P.S. Yu. Data Mining: An Overview from Database Perspective. IEEE Trans. on Knowledge and Data Engineering, 5(1):866-883, December 1996.

....retained. Initially, the method was proposed only for the case of transactional data. A lot of further research has been devoted to speeding up the algorithm and extending the approach to other scenarios [3, 8, 11, 12] An up to date survey on some of the work done in data mining may be found in [6]. 1.1 Contributions of this paper The main contributions of this work are as follows: 1) We propose an alternative model (called strongly collective itemset model ) to the large itemset method for association rule generation. 2) The large itemset technique is specially designed for sales ....

Chen M. S., Han J., and Yu P. S. Data Mining: An Overview from Database Perspective. IEEE Transactions on Knowledge and Data Engineering. Volume 8, Number 6, December 1996, 866-883.

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M. S. Chen, J. Han, P. S. Yu. "Data mining: An overview from a database perspective." IEEE Trans. on Knowledge and Data Eng., 8, 6, pp. 866--884, 1996.

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Chen MS, Han J, and Yu PS (1996) Data Mining: An Overview from a Database Perspective. IEEE Transactions on Knowledge and Data Engineering 8(6), pp. 866-883.

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Chen, M. and J. Han, "Data Mining: An Overview from a Database Perspective", IEEE Trans. on Knowledge and Data Engineering, 8(6), Dec 1996.

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Ming-Syan Chen, Jiawei Han, and Philip S. Yu. Data mining: An overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering, 8:866--883, December 1996.

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Ming-Syan Chen, Jiawei Han, and Philip S. Yu. Data mining: An overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6):866-883, December 1996.

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Ming-Syan Chen, Jiawei Han, and Philip S. Yu, Data mining: an overview from a database perspective, IEEE Trans. On Knowledge And Data Engineering 8 (1996), 866--883.

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M.S. Chen, J. Hart, and P.S. Yu. Data mining: An overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6):866-883, 1996.

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Chen, M.S., Han, J., and Yu, P.S., Data Mining: An Overview from Database Perspective, IEEE Transactions on Knowledge and Data Engineering, 8:6, pp. 866883, 1996

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M.S. Chen, J. Han and P.S. Yu, "Data mining: An overview from a database perspective", IEEE TKDE, 8(6), 1996.

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Chen, M.S., Han, J., Yu, P.S.: Data mining: an overview from a database perspective. IEEE Trans. On Knowledge And Data Engineering 8 (1996) 866--883

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M.S. Chen, J. Han, and P.S. Yu. Data mining: An overview from database perspective. IEEE Transactions on Knowledge and Data Eng., 8(6):866--883, December 1996.

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M.S. Chen, J. Han, and P.S. Yu. Data mining: An overview from database perspective. IEEE Transactions on Knowledge and Data Eng., 8(6):866--883, December 1996.

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M. Chen, J.Han and P. Yu. Data Mining: An Overview from the Database Perspective. IEEE Trans. On Knowledge and Data Engineering. Vol. 8. No. 6. December 1996.

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M.S. Chen, J. Han and P.S. Yu, "Data mining: An overview from a database perspective", IEEE TKDE, 8(6), 1996.

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M.-S. Chen, J. Han, and P. S. Yu. Data mining: an overview from a database perspective. IEEE Tr. On Knowledge And Data Engineering, 8(6):866-883, 1996.

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Ming-Syan Chen, Jiawei Han, and Philip S. Yu. Data mining - an overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6):866--883, 1996.

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Chen, M., Han, J., and Yu, P., (1996), "Data Mining: An Overview from a Database Perspective", IEEE Transactions on Knowledge and Data Engineering, Vol. 8, No. 6, (December), pp. 866-883.

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Ming-Syan Chen, Jiawei Han, and Philip S. Yu. Data mining: an overview from a database perspective. IEEE Trans. On Knowledge And Data Engineering, 8:866--883, 1996.

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Chen, M.S, Han, J., and Yu, P.S, "Data mining: an overview from a database perspective," IEEE Trans. on Knowledge and Data Engineering, Vol. 8, No. 6, Dec. 1996, pp.866-883.

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M.S. Chen, J. Han and P.S. Yu, "Data mining: An overview from a database perspective", IEEE TKDE, 8(6), 1996.

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M. S. Chen, J. Han and P. S. Yu, "Data mining: An overview from database perspective, " IEEE Trans. Knowledge and Data Eng. (TKDE ) 8(6), 866--883 (1996).

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M. Chen, J. Han and S. Yu, "Data Mining: An Overview from a Database Perspective", IEEE Transactions on Knowledge and Data Engineering , December 1996.

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Chen, M.S.; Han, J.; Yu, P.S. (1996): Data Mining: An Overview from a Database Perspective. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, Vol.8, No.6, pp.866-883.

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