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We consider the problem of discovering association rules between items in a large database of sales transactions. We present t wo new algorithms for solving this problem that are fundamentally diierent from the known algorithms. Empirical evaluation shows that these algorithms outperform the known algorithms by factors ranging from three for small problemsā¦ (More)

- Rakesh Agrawal, Ramakrishnan Srikant
- VLDB
- 1994

- Rakesh Agrawal, Ramakrishnan Srikant
- ICDE
- 1995

We are given a large database of customer transactions , where each transaction consists of customer-id, transaction time, and the items bought in the transaction. We i n troduce the problem of mining sequential patterns over such databases. We present three algorithms to solve this problem, and empirically evaluate their performance using synthetic data.ā¦ (More)

- Ramakrishnan Srikant, Rakesh Agrawal
- EDBT
- 1996

The problem of mining sequential patterns was recently introduced in 3]. We are given a database of sequences, where each sequence is a list of transactions ordered by transaction-time, and each transaction is a set of items. The problem is to discover all sequential patterns with a user-speciied minimum support, where the support of a pattern is the numberā¦ (More)

- Rakesh Agrawal, Heikki Mannila, Ramakrishnan Srikant, Hannu Toivonen, A. Inkeri Verkamo
- Advances in Knowledge Discovery and Data Mining
- 1996

- Rakesh Agrawal, Christos Faloutsos, Arun N. Swami
- FODO
- 1993

We propose an indexing method for time sequences for processing similarity queries. We use the Discrete Fourier Transform (DFT) to map time sequences to the frequency domain, the crucial observation being that, for most sequences of practical interest, only the rst few frequencies are strong. Another important observation is Parseval's theorem, whichā¦ (More)

- Rakesh Agrawal, Johannes Gehrke, Dimitrios Gunopulos, Prabhakar Raghavan
- SIGMOD Conference
- 1998

Data mining applications place special requirements on clustering algorithms including: the ability to find clusters embedded in subspaces of high dimensional data, scalability, end-user comprehensibility of the results, non-presumption of any canonical data distribution, and insensitivity to the order of input records. We present CLIQUE, a clusteringā¦ (More)

- Rakesh Agrawal, Ramakrishnan Srikant
- SIGMOD Conference
- 2000

A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models without access to precise information in individual dataā¦ (More)

We study the problem of answering ambiguous web queries in a setting where there exists a taxonomy of information, and that both queries and documents may belong to more than one category according to this taxonomy. We present a systematic approach to diversifying results that aims to minimize the risk of dissatisfaction of the average user. We propose anā¦ (More)

- Rakesh Agrawal, Jerry Kiernan, Ramakrishnan Srikant, Yirong Xu
- SIGMOD Conference
- 2004

Encryption is a well established technology for protecting sensitive data. However, once encrypted, data can no longer be easily queried aside from exact matches. We present an order-preserving encryption scheme for numeric data that allows any comparison operation to be directly applied on encrypted data. Query results produced are sound (no false hits)ā¦ (More)