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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...
NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences01:17

NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences

A pulse is a short burst of radio waves distributed over a range of frequencies that simultaneously excites all the nuclei in the sample. Upon passing a radio frequency pulse along the x-axis, the nuclei absorb energy corresponding to their Larmor frequencies and achieve resonance. This shifts the net magnetization vector from the z-axis toward the transverse plane. This angle of rotation of the magnetization vector, or the flip angle, is proportional to the duration and intensity of the pulse.
Double Resonance Techniques: Overview01:12

Double Resonance Techniques: Overview

Double resonance techniques in Nuclear Magnetic Resonance (NMR) spectroscopy involve the simultaneous application of two different frequencies or radiofrequency pulses to manipulate and observe two distinct nuclear spins. One important application of double resonance is spin decoupling, which selectively suppresses coupling with one type of nucleus while observing the NMR signal from another nucleus, simplifying the spectrum and enhancing resolution.
Spin decoupling is usually achieved by...

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Updated: May 11, 2026

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Toward Targeted Mining of RFM Patterns.

Xiaoye Chen, Wensheng Gan, Zefeng Chen

    IEEE Transactions on Neural Networks and Learning Systems
    |May 15, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces target recency, frequency, and monetary value (RFM) patterns for personalized customer segmentation. The efficient TaRFM algorithm accurately extracts these patterns, improving business insights.

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    Area of Science:

    • Data Mining
    • Business Intelligence
    • Customer Relationship Management

    Background:

    • Data mining is crucial for analyzing customer behavior in the information age.
    • Traditional Recency, Frequency, and Monetary (RFM) models segment customers but lack personalization.
    • There's a growing need for efficient and personalized customer analysis.

    Purpose of the Study:

    • To introduce the concept of target RFM patterns that incorporate user utility and interests.
    • To address the limitations of traditional RFM models in personalization and efficiency.
    • To develop an efficient algorithm for mining these target RFM patterns.

    Main Methods:

    • Formulated the problem of mining target RFM patterns.
    • Defined a specific mining order, the TaRFM order.
    • Proposed an efficient algorithm, TaRFM, with three pruning strategies.

    Main Results:

    • The TaRFM algorithm efficiently extracts all target RFM patterns without postprocessing.
    • Pruning strategies significantly reduce invalid operations and pattern generation.
    • Extensive experiments validated the algorithm's accuracy and efficiency on multiple datasets.

    Conclusions:

    • The TaRFM algorithm offers an effective solution for personalized customer segmentation.
    • This approach enhances business intelligence by identifying high-value customers more precisely.
    • The method improves efficiency and accuracy in data mining for customer behavior analysis.